Introduction: Antimicrobial resistance and the rapid spread of multiresistant bacteria represent one of the main public health problem in limited resources countries. This issue is significantly worsening since the COVID-19 pandemic due to the unreasonably increased antibiotics prescription to patients with confirmed SARS-CoV-2 infection. The aim of this study was to examine whether COVID-19 pandemic (2020, 2021) was associated with increased antibiotic consumption in inpatient and outpatient settings in the middle size urban region (Republic of Srpska/Bosnia and Herzegovina) in comparison to period before the pandemic (2019). Additionally, we aimed to determine antimicrobial resistance and the presence of multiresistant bacteria in the regional hospital (“Saint Apostol Luka” Hospital Doboj) in 2021. Methodology: The consumption of antibiotics in inpatient was calculated as Defined Daily Dose per one hundred of patient-days. The consumption of antibiotics in outpatient was calculated as Defined Daily Dose per thousand inhabitants per day. Resistance of bacteria to antibiotics is expressed as a rates and density for each observed antibiotic. The rate of resistance was calculated as a percentage in relation to the total number of isolates of individual bacteria. The density of resistance of isolated bacteria against a specific antibiotic was expressed as the number of resistant pathogens/1000 patient days. Results: Antibiotic consumption in hospital setting registered during 2019, 2020 and 2021 was as follows: carbapenems (meropenem: 0.28; 1.91; 2.33 DDD/100 patient-days, respectively), glycopeptides (vancomycin: 0.14; 1.09, 1.54 DDD/100 patient-days, respectively), cephalosporins (ceftriaxone: 6.69; 14.7; 14.0 DDD/100 patient-days, respectively) and polymyxins (colistin: 0.04; 0.25; 0.35 DDD/100 bed-days, respectively). Consumption of azithromycin increased drastically in 2020, and dropped significantly in 2021 (0.48; 5.61; 0.93 DDD/100 patient-days). In outpatient setting, an increase in the consumption of oral forms of azithromycin, levofloxacin and cefixime, as well as parenteral forms of amoxicillin-clavulanic acid, ciprofloxacin and ceftriaxone, was recorded. In 2021, antimicrobial resistance to reserve antibiotics in hospital setting was as follows: Acinetobacter baumanii to meropenem 66.0%, Klebsiella spp to cefotaxime 67.14%, Pseudomonas to meropenem 25.7%. Conclusion: Recent COVID-19 pandemic was associated with increased antibiotic consumption in inpatient and outpatient settings, with characteristic change of pattern of azithromycin consumption. Also, high levels of antimicrobial resistance to reserve antibiotics were registered in hospital setting with low prevalence of identified pathogen-directed antimicrobial prescription. Strategies toward combat antimicrobial resistance in the Doboj region are urgently needed.

Industrial production generates enormous amounts of wastewaters with a high content of organic and inorganic substances, which must be treated before discharging into a natural recipient to such a quality that it will not have a negative impact on the aquatic environment. This paper shows the possibility of applying a multi-stage process with Fenton reagents in combination with bentonite as an adsorbent in the treatment of ammonia-phenolic wastewater. The role of bentonite clay in this study was dye removal. The investigation was carried out under laboratory conditions, and the efficiency of the process was determined on the following parameters of COD, ammonia, phenol and thiocyanate. Also, the influence of the pH value, the concentration of oxidant hydrogen peroxide and catalyst iron sulphate heptahydrate was examined. The optimal values obtained for the pH, concentration of hydrogen peroxide and the catalyst iron sulphate heptahydrate was: 3; 30% and 23 g/l, where the efficiency of removal of the COD, ammonia, phenol and thiocyanate was: 96.42 %; 85.17 %; 100 % i 99.13 %.

In primary hyperoxaluria type 1 excessive endogenous production of oxalate and glycolate leads to increased urinary excretion of these metabolites. Although genetic testing is the most definitive and preferred diagnostic method, quantification of these metabolites is important for the diagnosis and evaluation of potential therapeutic interventions. Current metabolite quantification methods use laborious, technically highly complex and expensive liquid, gas or ion chromatography tandem mass spectrometry, which are available only in selected laboratories worldwide. Incubation of ortho-aminobenzaldehyde (oABA) with glyoxylate generated from glycolate using recombinant mouse glycolate oxidase (GO) and glycine leads to the formation of a stable dihydroquinazoline double aromatic ring chromophore with specific peak absorption at 440 nm. The urinary limit of detection and estimated limit of quantification derived from eight standard curves were 14.3 and 28.7 µmol glycolate per mmol creatinine, respectively. High concentrations of oxalate, lactate and L-glycerate do not interfere in this assay format. The correlation coefficient between the absorption and an ion chromatography tandem mass spectrometry method is 93% with a p value < 0.00001. The Bland–Altmann plot indicates acceptable agreement between the two methods. The glycolate quantification method using conversion of glycolate via recombinant mouse GO and fusion of oABA and glycine with glyoxylate is fast, simple, robust and inexpensive. Furthermore this method might be readily implemented into routine clinical diagnostic laboratories for glycolate measurements in primary hyperoxaluria type 1.

Paper covers image classification using the Keras API in TensorFlow. The dataset used is a set of labelled images consisting of characters from the Pokémon media franchise. In order to artificially generate additional data, the process of data augmentation has been applied on the initial dataset to reduce overfitting. A comparison between DenseNet-121, DenseNet-169 and DenseNet-201 has been made to observe which of the models scores a greater accuracy. A Graphics Processing Unit (GPU) has been set up to work with TensorFlow in order to efficiently train the model.

This paper presents the use of different prediction algorithms in order to recognise the popularity of a song. That recognition gives features that are directly affecting popularity of a song. For this research, data from several hundreds of the most popular songs were used in combination with songs that often appear on different playlists from different musicians. The reason for this mixing of songs is done to ensure that the model works as efficiently as possible by comparing popular songs features with those of that are no longer trending. The processing of the collected data gave an excellent insight into the importance of certain factors on the popularity of a certain song. As a result of research, month of release, acoustics and tempo were represented as features that are mostly correlated with popularity. Through the processing and analysis of a large amount of data, four models were created using different algorithms. Algorithms that were used are Decision Tree, Nearest Neighbour Classifier, Random Forest and Support Vector Classifier algorithms. The best results were achieved by training the model with the Decision Tree algorithm and accuracy of 100%.

Predictive modelling and AI have become a ubiquitous part of many modern industries and provide promising opportunities for more accurate analysis, better decision-making, reducing risk and improving profitability. One of the most promising applications for these technologies is in the financial sector as these could be influential for fraud detection, credit risk, creditworthiness and payment analysis. By using machine learning algorithms for analysing larger datasets, financial institutions could identify patterns and anomalies that could indicate fraudulent activity, allowing them to take action in real-time and minimize losses. This paper aims to explore the application of predictive models for assessing customer worthiness, identify the benefits and risks involved with this approach and compare their results in order to provide insights into which model performs best in the given context.

Web developers utilize responsive web design principles and frameworks to develop websites that are accessible on various platforms. As consumers often access websites through laptops, tablets, mobile phones, and desktop computers, it is necessary for the website to adjust its appearance according to the device's display frame width. However, the quality assurance process for responsive web pages is typically manual, time-consuming, and error prone. This study introduces ReDeCheck, an open-source automated website layout checking tool developed by Thomas A. Walsh, Gregory M. Kapfhammer, and Phil McMinn. The tool identifies the most common types of responsive design failures by utilizing a set of display frame widths based on the presentation of the website's dynamic layout, also known as the Responsive Layout Graph. This paper verifies the tool's functionality and its underlying concepts.

Mycotoxins have become a serious issue in the animal feed industry and have also affected the aquaculture industry. Mycotoxins can create serious health problems in aquatic and terrestrial animals, and their presence in agricultural products may result in significant economic losses. To reduce the impact of mycotoxins on Nile tilapia fry, two commercially available products—Organically Modified Clinoptilolite (OMC) and multi-component mycotoxin detoxifying agent (MMDA)—were used in this study. Six diets as treatments (T1 = Control (C); T2 = Control + OMC 2 g/kg (OMC); T3 = Control + MMDA 2 g/kg (MMDA); T4 = AFB1 0.5 mg/kg (AF); T5 = AFB1 0.5 mg/kg + 2 g/kg OMC (AFOMC); T6 = AFB1 0.5 mg/kg + MMDA 2 g/kg (AFMMDA)) with similar crude protein levels (35.75 ± 0.35%) were formulated and fed to Nile tilapia fry (1.97 ± 0.1 g) for a period of 84 days. These fish were housed in 18 aquaria (100 L) at a density of 50 fish/aquarium. The results from this study showed that MMDA significantly (p < 0.05) improved the survival of fish by 16% as compared to the control group. Nevertheless, growth parameters were not affected among the treatments. These results also indicated that protein intake was significantly higher in the control and OMC diet (T2) compared to aflatoxin B1-fed tilapia. The protein efficiency ratio (PER) was significantly higher in the AFMMDA as compared to the control and MMDA. A 14-day bacterial challenge test with Aeromonas hydrophila demonstrated that diets containing MMDA or OMC improved survival when AFB1 was present in the diet. Therefore, the supplementation of feed with MMDA or OMC is recommended to ameliorate the negative effects of AFB1 in Nile Tilapia feeds.

The protection and preservation of the privacy of personal data are one of the main requirements when it comes to an application dealing with the processing of such data. It is no different when it comes to information systems that store and process data about students in higher education systems. The public presentation of such data represents a serious threat to the safety of students as well as their status within the higher education system. For this reason, it is necessary to use the possibility of advanced technologies in order to raise data security to the highest level. One such technology that is able to provide transparency, security and data protection at a high level is blockchain technology. In this work, the Hyperledger Fabric distributed ledger private blockchain network was analyzed and its usability in terms of user rights management in higher education system was evaluated. Experimental analysis showed that such a platform has the ability to take advantage of private blockchain technologies in terms of user rights management and to provide security, flexibility and scalability of the system.

Maintaining and establishing transparency, security and privacy, when the data that should be included as part of documents that should serve as public educational documents in the labor market, are a challenging task, especially nowadays when we have more frequent cyber-attacks on public institutions. Setting up the security mechanisms of information systems that should store, process and show this type of data can be a very demanding job. For this reason, the introduction of new technologies in this area, such as blockchain technology, leads to considerable system and implementation relief. In this paper, private blockchain platforms are analyzed from the point of view of processing digital certificates or diplomas in the higher education system. An overview of the most popular platforms of this type is given. The most appropriate solution for these needs are discussed and proposed.

Shallow mountain lakes are highly sensitive to eutrophication. Cyanobacteria and microalgae in planktonic communities are the main producers in lake ecosystems, but stability of its communities is impacted by numerous factors. The aim of this study is to analyze seasonal diversity and community structure of cyanobacteria and microalgae in plankton and periphyton of the lake Paučko, physical and chemical properties of water and evaluate trophic status. The mountain lake Paučko is the shallow natural lake in Protected landscape Konjuh in northeastern Bosnia and Herzegovina. Sampling of net – phytoplankton, periphyton and water for physical and chemical analysis was caried in two seasons in 2018. Light microscopes and immersion objective (magnification 1000x) were used for the identification and quantification of microalgae. Non metric multimensional scaling and Simper analysis were used to describe communities in periphytic and planktic samples. In total, 70 taxa were identified. The most numerous were Bacillariophyta with 52, and Chlorophyta with 7 taxa. Seasonal dynamics in plankton communities were observed in the direction of shift of abundant Cyclotella meneghiniana, Dinobryon divergens, Peridinum cinctum and Ankistrodesmus fusiformus in spring season to Rabdoderma lineare and Pantocsekiella comensis in summer sampling season. Physical and chemical analysis of water revealed high values of total phosphorus, which correspond to the evaluated meso to eutrophic status of the lake calculated by Rott Trophic Index. The lake Paučko is under high pressure caused by the influx and retention of nutrients, which makes it susceptible to eutrophication. The results of the study provide the first insight into the diversity of cyanobacteria and microalgae for this lake and can be useful in planning of restoration measures in the context of ecological monitoring.

Abstract Gut microbiota is known as unique collection of microorganisms (including bacteria, archaea, eukaryotes and viruses) that exist in a complex environment of the gut. Recently, this has become one of the most popular areas of research in medicine because this plays not only an important role in disease development, but gut microbiota also influences drug pharmacokinetics. These alterations in drug pharmacokinetic pathways and drug concentration in plasma and blood often lead to an increase in the incidence of toxicological events in patients. This review aims to present current knowledge of the most commonly used drugs in clinical practice and their dynamic interplay with the host’s gut microbiota as well as the mechanisms underlying these metabolic processes and the consequent effect on their therapeutic efficacy and safety. These new findings set a foundation for the development of personalized treatments specific to each metabolism, maximizing drugs’ therapeutic effects and minimizing the side effects because they are one of the major limiting factors in treating patients.

With the rise in the number of robots in our daily lives, human-robot encounters will become more frequent. To improve human-robot interaction (HRI), people will require explanations of robots' actions, especially if they do something unexpected. Our focus is on robot navigation, where we explain why robots make specific navigational choices. Building on methods from the area of Explainable Artificial Intelligence (XAI), we employ a semantic map and techniques from the area of Qualitative Spatial Reasoning (QSR) to enrich visual explanations with knowledge-level spatial information. We outline how a robot can generate visual and textual explanations simultaneously and test our approach in simulation.

Yusong Tian,a,∗ Giovanni Calderini, Imogen Camp, Thibaud Idriss Carcone, Paul Mickael Chabrillat, Artur Cordeiro Oudot Choi, Francesco Crescioli, Jörn Große-Knetter, Šejla Hadžić, Shunsuke Iizaka, Christopher Krause, Lingxin Meng, f Koji Nakamura, Arnulf Quadt, Stefano Terzo, Ana Sofia Torrento Coello and Hua Ye II. Physikalisches Institut, Georg-August-Universität Göttingen, Friedrich-Hund-Platz 1, DE 37077 Göttingen, Germany LPNHE, Sorbonne Université, Université Paris Cité, CNRS/IN2P3, 4 place Jussieu, FR 75005 Paris, France Max-Planck-Institut für Physik (Werner-Heisenberg-Institut), Föhringer Ring 6, DE 80805 München, Germany Division of Physics and Tomonaga Center for the History of the Universe, Faculty of Pure and Applied Sciences, University of Tsukuba, 1 Chome-1-1 Tennodai, Tsukuba, Japan Fakultät Physik, Technische Universität Dortmund, Otto-Hahn-Straße 4, DE 44227 Dortmund, Germany f Physics Department, Lancaster University, Bailrigg, Lancaster LA1 4YW, United Kingdom KEK, High Energy Accelerator Research Organization, 1-1 Oho, Tsukuba, Japan Institut de Física d’Altes Energies (IFAE), Barcelona Institute of Science and Technology, UAB Campus, Edifici CN, ES 08193 Barcelona, Spain Detectors and Instrumentation Department, IJCLab – Laboratoire de Physique des 2 Infinis Irène Joliot-Curie, UMR 9012 – CNRS / Université Paris-Saclay / Université Paris Cité, 15 rue Georges Clémenceau, FR 91405 Orsay, France