When students enroll at universities, various datasets can be available to managers and teachers. Clustering techniques can be applied in order to divide the instances within those datasets into natural groups. In this paper, one clustering-based approach combined with attribute selection methods for identifying specific input dataset variables meaningful for the disjunction of distinct students' profiles has been proposed. Also, an analysis of the descriptive students' model obtained by the proposed methodology is performed.

New forms of communication are created as a result of the advancement of information and communication technologies, particularly the Internet and the WWW. These technologies introduce technological solutions as a response to ongoing difficulties. Relying on the predominant written form, besides its semantics, content on WWW must convey additional information like its structure, formatting, and relationships between its parts. Markup languages were born. Although markup languages addressed the issue at hand, they also raised several new issues, one of which is how to exchange material between disparate markup languages. In this paper, creating a meta-model of the two most commonly used markup languages, Markdown and HTML, is shown. The meta-model is created using the Eclipse Modeling Framework (EMF) ECore model, which relies on grammar obtained from markup languages. This meta-model simplifies comprehension of the relationship between markup language content and its model, which represents the first step towards the automatic transformation between models, i.e., the interchange of their content.

In research to determine the degree of interest in enrolling students in certain high schools, predictive analysis and comparison models are rarely used when classifying and processing different data. All this leads to large fluctuations in enrolment in secondary schools, where certain schools are unable to enrol numerous students who show an interest in a particular field. On the other hand, students lose interest in certain schools, which leads to the discontinuation of certain courses necessary for the needs of today's labour market. Institutions responsible for organizing the educational process do not sufficiently compare and connect teaching and non-teaching activities when analysing the talents and interests of elementary school students from different fields. The goal of this work is to predict the enrolment of students in secondary schools, using the classifiers of programming languages, based on the results that students express during regular classes in elementary schools.The results show that the accuracy of the data during the training of the Random Forest predictor is 52%, while in Wolfram Alpha it is 62%

The increase in the number of wind farms and their share in the total electrical energy generation leads to the need for a different approach to this source in cases where the stability of the power system is potentially impaired. With the development of different types of wind power plants, equipped with power electronics devices, there is the possibility of quick power management and injection, in conditions when it is needed, where a huge amount of accumulated kinetic energy can also be used. This paper presents the influence of a wind power plant equipped with a full-scale converter on the transient stability in cases of close and distant short circuits, during the outage of a heavily loaded line. Special attention was paid to the Rate of Change of Frequency (RoCoF) in the power system in cases with and without a wind farm where fast power injections were possible.

The contribution of renewable energy sources to the power system stability will have to be greater in the future. The problem will arise if the share of wind power plants in total production increases and large failures occur. Then, wind farms, which are often called inertia-less sources in the literature, will have to help maintain the frequency in a normal amount by changing the management method and based on fast PMU measurements. This can be done by using the synthetic inertia size, which is defined for sources that are derived from the system via energy converters and which do not participate in defining the total inertia of the system. This paper provides a better insight into the understanding of the concept of synthetic inertia, as well as insight into the current development of management and the application of synthetic inertia in maintaining the stability of the power system.

In this article, an upgraded version of CUDA-Quicksort - an iterative implementation of the quicksort algorithm suitable for highly parallel multicore graphics processors, is described and evaluated. Three key changes which lead to improved performance are proposed. The main goal was to provide an implementation with increased scalability with the size of data sets and number of cores with modern GPU architectures, which was successfully achieved. The proposed changes also lead to significant reduction in execution time. The execution times were measured on an NVIDIA graphics card, taking into account the possible distributions of the input data.

Air pollution represents one of the most complex problems of humanity. Traffic contributes significantly to this by emitting large amounts of harmful gases. This problem is particularly pronounced at urban intersections due to frequent changes in vehicle movement dynamics. This paper primarily presents the influence of intersection geometry on pollutant emissions levels. In addition, the influence of various traffic policies promoting greater use of public transport and zero-emission vehicles is also examined. The research combines the field part of recording existing intersections in Sarajevo, Bosnia and Herzegovina with traffic microsimulation. Detailed data on vehicles’ movements were obtained by advanced video processing using the DataFromSky tool, while the PTV Vissim 2022 and Bosch ESTM (2022) software were used to simulate traffic and estimate emissions at geometrically different intersections. The results showed that, in saturated traffic conditions, signalized intersections cause up to 50% lower emissions compared with two-lane and turbo roundabouts and that the impact of the geometric change is more significant than the impact of zero-emission vehicles. In unsaturated conditions, the differences in emissions at different intersections are negligible, with the highest reductions in pollution achieved by using zero-emission vehicles.

With the progress of technology and mankind, demand for different job positions has emerged. Reports indicating various new job types in the last decade are continuously published, giving us perspective on where we were a decade ago and where we are now. Most of the jobs are created around new technologies, yet not exclusively as jobs within technology production or usage (e.g., machine learning engineers, data scientists, app developers, etc.), but also as a type of jobs built atop of new technologies (e.g., social media manager, podcast producer, content moderator, etc.). With new job types, there is a gap between qualified employees and employers demands created. Taking into consideration trends that we have seen in the last years, more and more new job types will be created, and we can predict that this gap will become larger as time passes.

Vulnerability Assessment and Penetration Testing (VAPT) is an important component of an organization's overall security strategy. VAPT helps identify security vulnerabilities in a computer system, network, or web application, allowing organizations to take corrective measures to address these vulnerabilities and prevent potential security breaches. By conducting regular VAPT, organizations can improve their security posture and reduce the likelihood of successful attacks. In this paper Metasploit was used to show importance of regular vulnerability assessment of critical systems in order to discover vulnerabilities before attacker do it and exploit them. The authors showed Metasploit beside its usage to conduct a vulnerability assessment, it can be utilized by attackers to harm systems. VAPT is not a one-time event, but rather a ongoing process. As new vulnerabilities are discovered and new threats emerge, organizations need to regularly assess their systems to ensure they are protected.

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.

The Internet of Things (IoT) is considered a new paradigm that aims to connect a large number of devices. IoT is increasingly present in domains such as healthcare, transport, agriculture, and other industrial branches. An increasing number of IoT devices, as well as the amount of data, leads to increased energy consumption and a negative impact on the environment. Therefore, researchers are focusing on the concept of Green IoT that aims to increase energy efficiency and create a safe environment. The focus of this paper is on energy-efficient techniques within green data centers. Also, the performance evaluation of data centers was performed in the GreenCloud simulator for the optimal load of data centers in terms of energy efficiency and sustainability.

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.

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.