Extremely complex crisis that occurred after the proclamation of the COVID-19 pandemic in March 2020, contributed to the escalation of racism and xenophobia in the international arena. Their current rise must be observed from the perspective of the historically established pattern of connecting minorities, racial groups and certain communities with infectious diseases, which has made racist and xenophobic approaches and narratives indispensable constituents of responses to them. The stigma of the disease, as history confirms, is a permanent companion to the outbreak of infectious diseases, thus the coronavirus pandemic was no exception. The radicalization of public discourse through nativism, hatred and fear during the current health crisis, with the significant support of some mainstream media, was in the forefront contributed to by neo-nationalist forces. They exploited the insecurity and uncertainty generated by the pandemic to project fear of the other and different with their obligatory emanation as potential threats. Even though the COVID-19 pandemic contributed primarily to the reaffirmation of racism and xenophobia against the Chinese and Asians, which was supported by the coronavirus provenance, it undoubtedly paved the way for a general racist and xenophobic discourse.

Measurements and searches performed with the ATLAS detector at the CERN LHC often involve signatures with one or more prompt leptons. Such analyses are subject to `fake/non-prompt' lepton backgrounds, where either a hadron or a lepton from a hadron decay or an electron from a photon conversion satisfies the prompt-lepton selection criteria. These backgrounds often arise within a hadronic jet because of particle decays in the showering process, particle misidentification or particle interactions with the detector material. As it is challenging to model these processes with high accuracy in simulation, their estimation typically uses data-driven methods. Three methods for carrying out this estimation are described, along with their implementation in ATLAS and their performance.

BACKGROUND: Monitoring cardiac parameters is the fundamental aspect of every diagnostic process and is facilitated by electrocardiography (ECG) devices. This way, continuous state-of-the-art performance of ECG devices can be ensured. The new Medical Device Regulation (MDR) defines medical device post-market surveillance (PMS) as performed by independent, third-party, notified bodies more strategically in hope to improve traceability of device performance. However, there is still an apparent gap in terms of standardised conformity assessment testing methods. OBJECTIVE: This paper proposes a novel method for conformity assessment testing of ECG devices for post-market surveillance purposes. METHOD: The method was developed on the basis of International Organisation of Legal Metrology (OIML) guidelines and applied in healthcare institutions from 2018 to 2021. RESULTS: The developed method was validated in healthcare institutions of all levels. The results obtained during validation suggest that conformity assessment testing of the ECG device as a method used during PMS contributes to significant improvement in devices’ accuracy and reliability. CONCLUSION: A standardized approach in conformity assessment testing of ECG devices during PMS, besides increasing reliability of the devices, is the first step in the digital transformation of the management of these devices in healthcare institutions opening possibility for use of artificial intelligence.

The recently emerged novel coronavirus, “severe acute respiratory syndrome coronavirus‐2 (SARS‐CoV‐2),” caused a highly contagious disease called coronavirus disease 2019 (COVID‐19). It has severely damaged the world's most developed countries and has turned into a major threat for low‐ and middle‐income countries. Since its emergence in late 2019, medical interventions have been substantial, and most countries relied on public health measures collectively known as nonpharmaceutical interventions (NPIs). We aimed to centralize the accumulative knowledge of NPIs against COVID‐19 for each country under one worldwide consortium. International COVID‐19 Research Network collaborators developed a cross‐sectional online survey to assess the implications of NPIs and sanitary supply on the incidence and mortality of COVID‐19. The survey was conducted between January 1 and February 1, 2021, and participants from 92 countries/territories completed it. The association between NPIs, sanitation supplies, and incidence and mortality were examined by multivariate regression, with the log‐transformed value of population as an offset value. The majority of countries/territories applied several preventive strategies, including social distancing (100.0%), quarantine (100.0%), isolation (98.9%), and school closure (97.8%). Individual‐level preventive measures such as personal hygiene (100.0%) and wearing facial masks (94.6% at hospitals; 93.5% at mass transportation; 91.3% in mass gathering facilities) were also frequently applied. Quarantine at a designated place was negatively associated with incidence and mortality compared to home quarantine. Isolation at a designated place was also associated with reduced mortality compared to home isolation. Recommendations to use sanitizer for personal hygiene reduced incidence compared to the recommendation to use soap. Deprivation of masks was associated with increased incidence. Higher incidence and mortality were found in countries/territories with higher economic levels. Mask deprivation was pervasive regardless of economic level. NPIs against COVID‐19 such as using sanitizer, quarantine, and isolation can decrease the incidence and mortality of COVID‐19.

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.

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.

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.

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.

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.