Introduction Domestic violence is toxic to society. With approximately one in three women on average falling victim to domestic violence, systematic solutions are needed. To further complicate the issue, mounting research shows that COVID-19 has further exacerbated domestic violence across the world. Situations could be even more pronounced in countries like China, where though domestic violence is prevalent, there is a dearth of research, such as intervention studies, to address the issue. This study investigates key barriers to domestic violence research development in China, with a close focus on salient cultural influences. Methods A review of the literature on domestic violence in China in PubMed, PsycINFO, and Scopus was conducted to answer the research question. The search was focused on three themes, domestic violence, China, research, and cultural influences. Results The study findings show that categorizing domestic violence as a “family affair” is a key barrier to domestic violence research development in China—an incremental hindrance that prevents the public and policymakers from understanding the full scale and scope of domestic violence in China. In addition to abusers, witnesses, and victims, even law enforcement in China often dismisses domestic violence crimes as “family affairs” that resides outside the reach and realm of the law. The results indicated that mistreating domestic violence crimes as “family affairs” is a vital manifestation of the deep-rooted cultural influences in China, ranging from traditional Confucian beliefs in social harmony to the assumed social norms of not interfering with other people's businesses. Conclusion Domestic violence corrupts public health and social stability. Our study found that dismissing domestic violence cases as “family affairs” is an incremental reason why China's domestic violence research is scarce and awareness is low. In light of the government's voiced support for women's rights, we call for the Chinese government to develop effective interventions to timely and effectively address the domestic violence epidemic in China.

This paper analyzes the constitutional position of Bosnia and Herzegovina (BiH) between constitutional nationalism and human rights. In its judgments in Sejdić and Finci v. BiH, and Zornić v. BiH, the European Court of Human Rights (ECtHR) found that the BiH Constitution was not in line with the European Convention on Human Rights and Fundamental Freedoms (ECHR). Namely, the privilege of the three constituent peoples in the Constitution of BiH, the ECHR was assessed as contrary to the prohibited discrimination prescribed by the ECHR. In this sense, the paper analyzes the current Bosnian constitutional model of a form of consociational democracy or constitutional nationalism. The authors analyze the presumption of the ECtHR in terms of the (non) existence of a legitimate goal for maintaining discriminatory provisions in the Constitution of BiH. Also, the paper analyzes the importance of the implementation of the ECtHR judgment for the future of the rule of law in BiH.

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.

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.

In this paper, we deal with the potential applications of Artificial Intelligence in educational institutions. One of the main goals of applying the mentioned technology is to make the education system more efficient if possible. Based on research in this area in the world, our impression is that there is a long-term plan for introducing AI in schools, as a new kind of learning standard. The idea is not for artificial intelligence to completely take over the domain of education, but for some processes to be better organized or improved, so that the result of learning would be of better quality. New technologies, in the form of modern learning platforms that incorporate AI as one of the most important components of the system, significantly improve the communication and research work of students. It is evident that new generations are coming whose experience will be significantly different from ours, and that the new technologies from the domain of AI that we consider in this paper will significantly affect the design processes in the future.

Artificial intelligence technology is rapidly advancing year by year, and thus the possibilities of using artificial intelligence in every branch of science. The importance of artificial intelligence and its components has been known for a long time. They are seen as tools and techniques that make the world a better place With their simple and everyday techniques, they make the world a mistake-free place. These technologies and applications are not only related to our general and everyday life, but also affect and have significance for other domains as well. The paper will analyze the application of artificial intelligence and its influence today.

Abstract: The paper presents results of the measurements of the sulfur dioxide (SO2) and nitrogen dioxide (NO2) concentration and meteorological parameters: temperature, air pressure, relative humidity and wind speed. The data were collected from January 2019 to December 2020 at two stations, namely Center and Heating plant, in the City of Bijeljina, Republic of Srpska, Bosnia and Herzegovina. SO2 and NO2 are one of the major air pollutants that could negatively affect the human health. Levels of SO2 and NO2 in air samples and meteorological variables from urban zone of Bijeljina were determined at both localities, which represent a highly-populated area with intensive traffic. This topic has not been studied up to now in Bijeljina, although the recent research data indicates that there is a correlation between meteorological parameters and air pollutants. Statistical analysis confirms direct corelation between SO2 and NO2 and meteorological parameters, specially temperature in locality Center (r = -0.639), the wind speed in locality Heating plant (r = 0.399) and relative humidity (r = 0.162). Correlation of NO2 with temperature is not confirmed in both localities. The wind speed increase is followed by rises of the NO2 concentration values and vice versa. Correlation of NO2 with pressure is confirmed in locality Center (r = 0.128) but it is not confirmed in locality Heating plant. Correlation between NO2 and relative humidity found to be negative in locality Center (r = -0.062). These parameters are the most important meteorological factors influencing the variation in SO2 and NO2 concentration in the air during the research. Depending on the obtained correlation, meteorological parameters had a positive or negative impact on air pollution.

The recent B-meson anomalies are coherently explained at the TeV scale by 4321 gauge models with hierarchical couplings reminiscent of the Standard Model Yukawas. We show that such models arise as the low-energy limit of a complete theory of flavor, based on a warped fifth dimension where each Standard Model family is quasi-localized in a different brane. The Higgs is identified as a pseudo-Nambu-Goldstone boson emerging from the same dynamics responsible for 4321 symmetry breaking. This novel construction unifies quarks and leptons in a flavor non-universal manner, provides a natural description of flavor hierarchies, and addresses the electroweak hierarchy problem.

The aim of the study was to determine the differences in the level of fat, sugar and body structure based on the level of functional abilities. The sample of respondents are students of the University of Bihać who also completed the shuttle run test (BEEPT). T-test for independent samples revealed the existence of statistically significant differences between the arithmetic means of the two groups of subjects for (AMAS, p = 0 .011), (BMI p = 0 .000), (FAT%, p = 0.000), (FMKG p) = 0.000). A statistically significant difference was also found in the variable triglycerides (TRIGL p = 0.019), while in the other variables no statistically significant difference was found in the two groups of subjects in favor of subjects who had better results in functional abilities. The coefficient of discriminant canonical correlation is (0.512), as is Wilks lambda, (0.738), which indicates very high discrimination between groups (sig. 000). The greatest contribution to the formation of the discriminatory function was given by the variables FMKG - .801, FATPR -.760, BMI - .707, AMAS - .390, TRIGL - .358, HOLE - .235. The centroids of the groups show a large distance between the results of the groups because they are located at both ends of the coordinate system. The first group consists of positive results of a total of 7 variables, which means that the respondents of the first group had significantly better results in these variables. Based on the results, it can be concluded that the increase in cholesterol triglycerides and some parameters of body structure affected the level of health status as well as body composition in students.

The Sarajevo Canton Winter Field Campaign 2018 (SAFICA) was a project that took place in winter 2017–2018 with an aim to characterize the chemical composition of aerosol in the Sarajevo Canton, Bosnia and Herzegovina (BiH), which has one of the worst air qualities in Europe. This paper presents the first characterization of the metals in PM10 (particulate matter aerodynamic diameters ≤10 μm) from continuous filter samples collected during an extended two-months winter period at the urban background Sarajevo and remote Ivan Sedlo sites. We report the results of 18 metals detected by inductively coupled plasma mass spectrometry (ICP-MS) and electrothermal atomic absorption spectrometry (ETAAS). The average mass concentrations of metals were higher at the Sarajevo site than at Ivan Sedlo and ranged from 0.050 ng/m3 (Co) to 188 ng/m3 (Fe) and from 0.021 ng/m3 (Co) to 61.8 ng/m3 (Fe), respectively. The BenMAP-CE model was used for estimating the annual BiH health (50% decrease in PM2.5 would save 4760+ lives) and economic benefits (costs of $2.29B) of improving the air quality. Additionally, the integrated energy and health assessment with the ExternE model provided an initial estimate of the additional health cost of BiH’s energy system.

Estimating conditional average treatment effects (CATE) is challenging, especially when treatment information is missing. Although this is a widespread problem in practice, CATE estimation with missing treatments has received little attention. In this paper, we analyze CATE estimation in the setting with missing treatments where unique challenges arise in the form of covariate shifts. We identify two covariate shifts in our setting: (i) a covariate shift between the treated and control population; and (ii) a covariate shift between the observed and missing treatment population. We first theoretically show the effect of these covariate shifts by deriving a generalization bound for estimating CATE in our setting with missing treatments. Then, motivated by our bound, we develop the missing treatment representation network (MTRNet), a novel CATE estimation algorithm that learns a balanced representation of covariates using domain adaptation. By using balanced representations, MTRNet provides more reliable CATE estimates in the covariate domains where the data are not fully observed. In various experiments with semi-synthetic and real-world data, we show that our algorithm improves over the state-of-the-art by a substantial margin.

INTRODUCTION Additive production refers to the process of prototyping, which allows the production of highly complex medical devices and products. Interpretation of additive manufacturing (AM) material in Computed Tomography (CT) has not been widely investigated. The aim of this study was to determine the CT number values of commercially available fused deposition modelling (FDM) and stereolithography (SLA) AM materials. METHODS Total of 15 AM materials, 7 FMD and 8 SLA, were selected and scanned on CT to determine the HU value and appearance on the images. All test object were designed as rectangular blocks and after their production physical description were calculated. AM materials were scanned on CT operating at 80, 100, 120 and 135 kV. RESULTS All materials correspond to a certain human tissue and they have uniformity when printed with 100% infill. CT number ranged from a minimum of -188.0 HU to a maximum of 189.1 HU, for FDM materials, and from -15.8 HU to 167.3 HU, for SLA materials. CONCLUSION Knowing the CT number of an AM materials can allow the design of medical or rehabilitation products with a specific appearance on CT images. Analysed and collected data can find application in the design and manufacture of immobilization devices that can be easily distinguished from other materials or human tissue. IMPLICATIONS FOR PRACTICE This study provides information that can be used in the design and fabrication of anthropomorphic diagnostic and therapeutic phantoms. There is significant potential for the use of AM material for sophisticated test objects when used in medical image modality testing. Knowing actual CT numbers of frequently used AM materials allows manufacturing anthropomorphic phantoms to investigate radiation doses in diagnostic radiology and radiotherapy.