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.

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 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.

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.

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.

Background: Thimerosal is an organomercury compound with high mercury content. Thimerosal is located in the market as an effective bacteriostatic in a series of pharmaceutical products, ophthalmic and nasal products, immunoglobulin preparations, and as a preservative in vaccines. Aims and Objectives: Since it is a compound with living content, this study aimed to examine the genotoxic and cytotoxic potential of thimerosal in human lymphocytes culture. Materials and Methods: We used chromosome aberration analysis and cytokinesis-block micronucleus cytome (CBMN-Cyt) assay to test its genotoxic and cytotoxic potential in human lymphocyte culture. Results: Results showed that the frequency of structural chromosome aberrations and CBMN-cyt assay was significantly increased in treated cultures (1 μg/ml and 0.5 μg/ml) compared to the negative control. Conclusion: Obtained results and statistical analysis show that thimerosal is genotoxic and cytotoxic in human lymphocytes in tested concentrations.

Benzophenone (BP) type UV filters are common environmental contaminants that are posing a growing health concern due to their increasing presence in water. Different studies have evidenced the presence of benzophenones (BP, BP-1, BP-2, BP-3, BP-4, BP-9, HPB) in several environmental matrices, indicating that conventional technologies of water treatment are not able to remove them. It has also been reported that these compounds could be associated with endocrine-disrupting activities, genotoxicity, and reproductive toxicity. This review focuses on the degradation kinetics and mechanisms of benzophenone-type UV filters and their degradation products (DPs) under UV and solar irradiation and in UV-based advanced oxidation processes (AOPs) such as UV/H2O2, UV/persulfate, and the Fenton process. The effects of various operating parameters, such as UV irradiation including initial concentrations of H2O2, persulfate, and Fe2+, on the degradation of tested benzophenones from aqueous matrices, and conditions that allow higher degradation rates to be achieved are presented. Application of nanoparticles such as TiO2, PbO/TiO2, and Sb2O3/TiO2 for the photocatalytic degradation of benzophenone-type UV filters was included in this review.

Sludge generated in wastewater treatment processes must be treated in an adequate manner, and therefore disposed of in an environmentally friendly manner. The biggest obstacle to the efficient use of sludge is the high water content, therefore the development of methods to accelerate the sludge dewatering process is particularly important. For the purposes of the research, the waste sludge created in the process of purifying ammoniaphenol wastewater in the GIKIL factory was used. Sludge conditioning was performed by adding a commercial flocculant (0.1 % solution) in combination with pyrophyllite, kaolin clay and shredded cardboard. According to earlier research, the volume of waste sludge with the addition of a suitable flocculant could be reduced by more than 5 times compared to the initial amount, leaving behind a large amount of separated water that can be returned to the process. The results show that the addition of commercial flocculant in the amount of 0.8 % gives the best results (reduction of sludge volume by 78.8 %). A lower percentage was found in samples with a mixture of flocculants with pyrophyllite (78 %) and kaolin (77.6 %), while the combination of flocculants with waste cardboard was ineffective (64.4 %). Addition of flocculant to waste sludge resulted in a decrease in specific filtration resistance (1.15x107 s2/g). A decrease in specific resistance was also observed in flocculant/kaolin clay (0.8x107 s2/g) and flocculant/cardboard (1.09x107 s2/g) samples. Sludge conditioning also resulted in a reduction of suspended solids in the neonate compared to settled raw sludge without additives.

Background: Psoriasis can be described as a T-cell-mediated disease, with a complex role for variety of cytokines and other factors. Among the inflammatory molecules influencing the keratinocites, TNF-α appears critical in sustaining most of the clinical manifestations of psoriasis. It is postulated that changes in cytokine production both locally and systemically could be useful in monitoring disease activity. Objective: The aim of the study was to evaluate serum levels of tumor necrosis factor alpha (TNF-α) in patients with psoriasis and the healthy subjects, and also to assess a possible association between TNF-α, clinical type and severity of disease. Methods: We studied the levels of serum TNF-α in 60 patients with psoriasis and in the serum of helthy 20 controls. According to the clinical type of disease, patients with psoriasis were divided into four groups: chronic plaque psoriasis (CPP), erythrodermic psoriasis (EP), pustular psoriasis (PP) and psoriatic arthritis (PA). Blood samples were collected from all psoriasis patients and from healthy control subjects. Serum level of TNF-α were measured by an enzyme-linked immunosorbent assay (ELISA) technique. The severity of CPP was assessed by Psoriasis Area and Severity Index (PASI). Results: Serum levels of TNF-α in patients with psoriasis were significialy higher than in the control group (3.25+1.74 pg/mL vs 0.20+0.01pg/mL, respectively). Significantly elevated serum TNF-α was in patients with PP type (7.39+6.92 pg/mL). There was statistically significant difference between the mean level of TNF-ɑ compared to the clinical type of psoriasis (p<0.05). The mean PASI score in patients with CPP was 0.56±12.45. It was not found statistically significant correlation between serum level of TNF-ɑ and PASI score in patients with CPP (p>0,05). Conclusion: Our results have demonstrated the imortance of determining serum levels of TNF-ɑ in patients with psoriasis. Further investigations are required to clarify the pathogenic role and clinical significance of TNF-ɑ, and these findings may provide important clues to assist in the development of new therapeutic strategies for patients with psoriasis.

Age estimation has become inordinately significant for human beings for many reasons, such as detecting legal and criminal responsibility and other social events like a marriage license, birth certificate, etc. This paper aims to decide on the most desirable machine learning algorithm (from conventional machine learning algorithms to deep learning) for dental age estimation based on buccal bone level. The database consisted of 150 CBCT images (73 males and 77 females) from an existing base of the Faculty of Dental Medicine with Clinics, University of Sarajevo, aged 20-69. Results were obtained using the Waikato Environment for Knowledge Analysis (Weka), machine learning software in Java. Left and Right Buccal Alveolar Bone Levels are increasing with age, so they showed to be the most important attributes, especially the latter. Random Forest classifier provided the greatest result with the correlation coefficient of 0.803 and the mean absolute error of 6.022. We have also shown that considering sinus-related features can be a significant addition to the databases. Our paper is probably one of the first studies where regression algorithms based on the Support Vector Machines and Random Forest were utilized.