Human monkeypox represents a relatively underexplored infection that has received increased attention since the reported outbreak in May 2022. Due to its clinical similarities with human smallpox, this virus represents a potentially tremendous health problem demanding further research in the context of host-pathogen interactions and vaccine development. Furthermore, the cross-continental spread of monkeypox has reaffirmed the need for devoting attention to human poxviruses in general, as they represent potential bioterrorism agents. Currently, smallpox vaccines are utilized in immunization efforts against monkeypox, an unsurprising fact considering their genomic and phenotypic similarities. Though it offers long-lasting protection against smallpox, its protective effects against human monkeypox continue to be explored, with encouraging results. Taking this into account, this works aims at utilizing in silico tools to identify potent peptide-based epitopes stemming from the variola virus and monkeypox virus proteomes, to devise a vaccine that would offer significant protection against smallpox and monkeypox. In theory, a vaccine that offers cross-protection against variola and monkeypox would also protect against related viruses, at least in severe clinical manifestation. Herein, we introduce a novel multi-epitope mRNA vaccine design that exploits these two viral proteomes to elicit long-lasting humoral and cellular immunity. Special consideration was taken in ensuring that the vaccine candidate elicits a Th1 immune response, correlated with protection against clinically severe disease for both viruses. Immune system simulations and physicochemical and safety analyses characterize our vaccine candidate as antigenically potent, safe, and overall stable. The protein product displays high binding affinity towards relevant immune receptors. Furthermore, the vaccine candidate is to elicit a protective, humoral and Th1-dominated cellular immune response that lasts over five years. Lastly, we build a case about the rapidity and convenience of circumventing the live attenuated vaccine platform using mRNA vaccine technology.

U radu je izvedena komparativna analiza ustavne pozicije i organizacije lokalne samouprave u glavnim gradovima: Ljubljana, Zagreb, Beograd, Podgorica i Sarajevo. U komparativnoj analizi modela lokalne samouprave u glavnim gradovima su posebno uzeti u obzir aspekti ustavnog statusa glavnog grada kao jedinice lokalne samouprave; teritorijalna organizacija; obim samoupravnih nadležnosti u vršenju javnih poslova te institucionalna organizacija gradskih vlasti. Cilj rada je da se komparativnom analizom identificiraju nedostaci u ustavnoj poziciji i organizaciji lokalne samouprave u Gradu Sarajevu. Rezultati komparativne analize mogu biti od koristi u oblikovanju prijedloga institucionalnog modela reforme i organizacije lokalne samouprave u Gradu Sarajevu.

BIH lags far behind in development of the knowledge society and economy. It has a far smaller number of skilled workers and researchers compared to comparator countries, i.e. small countries of Central and Southeast Europe. Moreover, there are only 144 researchers employed in research and development in the economy. The City of Sarajevo can contribute to its own development and the development of BiH if it positions itself as the main information and communication capital. This can be achieved if the City and its municipalities decide to develop the Sarajevo Innovation District. The first step in this direction could be the establishment of the Council for Promotion of Software Production and Export and the establishment of an information and communication technology and innovation excellency centre. The experience of Ireland in establishing software study centres and India in forming Indian Silicon Valley in Bangalore is of valuable benefit. On this basis, Sarajevo would develop as a strong knowledge city and over time become an international centre for software developers, students and start-ups and as a strong innovation district. Sarajevo would become a city that attracts talent and knowledge workers. This would become a model for the development of other knowledge cities in Bosnia and Herzegovina and contribute to the growth of an upper-middle-income country into a high-income country.

Though semantic segmentation has been heavily explored in vision literature, unique challenges remain in the remote sensing domain. One such challenge is how to handle resolution mismatch between overhead imagery and ground-truth label sources, due to differences in ground sample distance. To illustrate this problem, we introduce a new dataset and use it to showcase weaknesses inherent in existing strategies that naively upsample the target label to match the image resolution. Instead, we present a method that is supervised using low-resolution labels (without upsampling), but takes advantage of an exemplar set of highresolution labels to guide the learning process. Our method incorporates region aggregation, adversarial learning, and self-supervised pretraining to generate fine-grained predictions, without requiring high-resolution annotations. Extensive experiments demonstrate the real-world applicability of our approach.

In this research, a physicochemical analysis of the industrial wastewater from a factory that produces maleic anhydride was performed. Based on the conducted analysis (pH, electrical conductivity, density of the liquid phase, boiling point of the waste suspension, chemical as well as biological oxygen demand, and dry matter), it can be concluded that the waste stream obtained at the outlet pipe from the plant resulting from the production of maleic anhydride requires appropriate treatments. Some of the parameters measured, e.g. pH (0.97±0.06), boiling point (106.8±1.3°C) and acidity, indicate the presence of organic acids such as fumaric and maleic acids, which are formed during the production of maleic anhydride. The possibility of extracting crystals by adding urea and thiourea followed by forced cooling in a heat exchanger was investigated. The most effective method was the addition of thiourea when the most significant amount of crystals was obtained, namely 17.29 wt%. The addition of thiourea in combination with forced cooling greatly facilitates the process of separating the solid and liquid phases of the waste suspension, which could later be adequately treated by physical, chemical or biological methods.

Background: Healthcare workers at all levels of the healthcare system are at the front line of the response to the COVID-19 epidemic and are consequently more exposed to risk of infection. To examine the characteristics of the healthcare workers morbidity during the COVID-19 pandemic in the area of Central Bosnia Canton.Methods: This research includes all healthcare workers of this Canton (n=2276) in the period from March 2020 to March 2022. A total of 666 health workers tested positive (RT-PCR method) for severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) in healthcare institutions.Results: The average age of the patients was 45.16 (±11.93) (range 18-77 years old), with a median of 45 years old. Of the total number of positive patients, 68.2% were women, 165 or 24.77% were doctors, and 57.80% were other medical workers. Interactions with infected colleagues accounted for 28.4% of infections, 22.2% of infections occurred during patient care, 36.3% outside a healthcare facility, and in 13.1% the mode of infection was not confirmed. Due to the severity of the clinical status, a total of 74 people were hospitalized with a hospitalization rate of 11.11 (95% CI 8.78-13.87). The second positivity of the test (by RT-PCR method) was after 12.34 months from the first infection (mean=12.34: SD±4.270; median=13).Conclusions: High rates of morbidity among healthcare workers certainly have a significant, long-term impact on the healthcare provision, especially in healthcare systems where there is a pronounced lack of professional workers. 

Predmet ovog rada je normativno i institucionalno uređenje glavnih gradova u bosanskohercegovačkom okruženju (Beograda i Zagreba), s posebnim osvrtom na Sarajevo, glavni grad Bosne i Hercegovine. Historijski razvoj ovih glavnih gradova odvijao se različito, iako su gradovi jedan duži vremenski period bili u jedinstvenoj državi. Ipak, drugačiji uticaji i pravci njihovog razvoja različito su ispisivali njihovu historiju. Neki od njih bili su uspješnog razvoja, a neki stagnirali. Višegodišnji pokazatelji o radu i aktivnostima Grada Sarajeva pokazuju njegove određene dobre, ali i slabe rezultate. Jačanje administrativnih kapaciteta u Gradu Sarajevu, te općinama koje ulaze u njegovu organizaciju i teritorij, ne doprinose dovoljno u rješavanju tekućih problema grada i gradskih općina. Da li tim rezultatima doprinosi neprecizna ili nedovoljna normativna uređenost nadležnosti grada Sarajeva ili sukobljavanje nadležnosti općina koje participiraju u Gradu Sarajevu i samog Grada Sarajeva, pitanje je za temeljito proučavanje ove materije. Zato je autor sačinio dublju analizu normativne i institucionalne uređenosti glavnih gradova Beograda, Zagreba i Sarajeva, te uporednom analizom došao do određenih pokazatelja i zaključaka, što čine odgovore na glavna pitanja koja se razmatraju u ovom radu.

Specific emitter identification (SEI) plays an increasingly crucial and potential role in both military and civilian scenarios. It refers to a process to discriminate individual emitters from each other by analyzing extracted characteristics from given radio signals. Deep learning (DL) and deep neural networks (DNNs) can learn the hidden features of data and build the classifier automatically for decision making, which have been widely used in the SEI research. Considering the insufficiently labeled training samples and large-unlabeled training samples, the semi-supervised learning-based SEI (SS-SEI) methods have been proposed. However, there are few SS-SEI methods focusing on extracting the discriminative and generalized semantic features of radio signals. In this article, we propose an SS-SEI method using metric-adversarial training (MAT). Specifically, pseudo labels are innovatively introduced into metric learning to enable semi-supervised metric learning (SSML), and an objective function alternatively regularized by SSML and virtual adversarial training (VAT) is designed to extract discriminative and generalized semantic features of radio signals. The proposed MAT-based SS-SEI method is evaluated on an open-source large-scale real-world automatic-dependent surveillance–broadcast (ADS-B) data set and Wi-Fi data set and is compared with the state-of-the-art methods. The simulation results show that the proposed method achieves better identification performance than existing state-of-the-art methods. Specifically, when the ratio of the number of labeled training samples to the number of all training samples is 10%, the identification accuracy is 84.80% under the ADS-B data set and 80.70% under the Wi-Fi data set. Our code can be downloaded from https://github.com/lovelymimola/MAT-based-SS-SEI.

Background and Objectives: Coronavirus disease 2019 (COVID-19) is a novel infectious disease that has spread worldwide. As of 5 March 2020, the COVID-19 pandemic has resulted in approximately 111,767 cases and 6338 deaths in the Republic of Srpska and 375,554 cases and 15,718 deaths in Bosnia and Herzegovina. Our objective in the present study was to determine the characteristics and outcomes of critically ill pregnant/postpartum women with COVID-19 in the Republic of Srpska. Materials and Methods: The retrospective observational study of prospectively collected data included all critically ill pregnant/postpartum women with COVID-19 in a university-affiliated hospital between 1 April 2020 and 1 April 2022. Infection was confirmed by real-time reverse transcriptase polymerase chain reaction (RT-PCR) from nasopharyngeal swab specimens and respiratory secretions. Patients’ demographics, clinical and laboratory data, pharmacotherapy, and neonatal outcomes were analysed. Results: Out of the 153 registered pregnant women with COVID-19 treated at the gynaecology department of the University Clinical Centre of the Republic of Srpska, 19 (12.41%) critically ill pregnant/postpartum women (median age of 36 (IQR, 29–38) years) were admitted to the medical intensive care unit (MICU). The mortality rate was 21.05% (four patients) during the study period. Of all patients (19), 14 gave birth (73.68%), and 4 (21.05%) were treated with veno-venous extracorporeal membrane oxygenation (vvECMO). Conclusions: Fourteen infants were born prematurely and none of them died during hospitalisation. A high mortality rate was detected among the critically ill pregnant/postpartum patients treated with mechanical ventilation and vvECMO in the MICU. The preterm birth rate was high in patients who required a higher level of life support (vvECMO and ventilatory support).

In this paper we observed the global dynamics and the occurrence of a certain bifurcation for the corresponding values of a certain rational difference equation of the second order with analyzed quadratic terms. The analysis of the local stability of the unique equilibrium point, as well as the unique periodic solution of period two, was performed in detail. The constraint of the equations on both sides for the corresponding values of the parameters is proved and on this basis the global stability is analyzed. The existence of Neimark-Sacker bifurcation with respect to the arrangement of equilibrium points has been proven. Thus, the basins of attraction have been determined in full for all the positive values of the parameters and all the positive initial conditions.

In recent years, autonomous systems have become an important research area and application domain, with a significant impact on modern society. Such systems are characterized by different levels of autonomy and complex communication infrastructures that allow for collective decision-making strategies. There exist several publications that tackle ethical aspects in such systems, but mostly from the perspective of a single agent. In this paper we go one step further and discuss these ethical challenges from the perspective of an aggregate of autonomous systems capable of collective decision-making. In particular, in this paper, we propose the Caesar approach through which we model the collective ethical decision-making process of a group of actors—agents and humans, as well as define the building blocks for the agents participating in such a process, namely Caesar agents. Factors such as trust, security, safety, and privacy, which affect the degree to which a collective decision is ethical, are explicitly captured in Caesar . Finally, we argue that modeling the collective decision-making in Caesar provides support for accountability.