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.

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.

Since depletion of natural resources and the amount of construction and demolition waste have overcome the socially and environmentally acceptable level, the construction industry must address this issue and reduce its impact on the environment. A step towards sustainability in the construction industry is the application of recycled aggregates and supplementary cementitious materials as integral components of concretes, which provides conserving natural aggregates and waste reduction. This study adopts a holistic approach to producing green self-compacting concrete with the highest portion of recycled aggregate as a replacement for natural aggregate and fly ash as filler. Based on the particle packing density method, four series of self-compacting concrete were prepared: the first series was made with natural fine and coarse aggregate, the second series was made with fine natural aggregate and recycled coarse aggregate, the third with 50 % (by mass) of fine natural aggregate replaced by recycled fine aggregate and recycled coarse aggregate, and the fourth series completely with recycled fine and coarse aggregate. The content of fly ash remained constant. Regardless of the expected decrease of workability in a fresh state with the increase of the recycled aggregate content, all series exceeded the requirements set for the hardened structural concrete.

As the demand for sustainable and renewable energy sources grows, the use photovoltaic (PV) systems have seen rise in popularity and recognition. The performance of PV systems is influenced by numerous factors such as solar irradiance, temperature, and the tilt angle of the PV modules. Among these factors, the tilt angle of the PV modules plays a crucial role in determining the amount of energy that can be generated by a PV system. This paper explores the impact of tilt angle on the output and performance of grid-connected PV systems by using the software PVsyst. The study will examine how different tilt angles affect the energy yield, electrical characteristics, and performance ratio of PV system. A study was conducted to compare the performance of a PV system with fixed tilt angle versus seasonal tilt arrangement. The results showed that a seasonal tilt arrangement led to improved performance and increased electricity generation.

Objectives The addition of CT-derived fractional flow reserve (FFR-CT) increases the diagnostic accuracy of coronary CT angiography (CCTA). We assessed the impact of FFR-CT in routine clinical practice on clinical decision-making and patient prognosis in patients suspected of stable coronary artery disease (CAD). Methods This retrospective, single-center study compared a cohort that received CCTA with FFR-CT to a historical cohort that received CCTA before FFR-CT was available. We assessed the clinical management decisions after FFR-CT and CCTA and the rate of major adverse cardiac events (MACEs) during the 1-year follow-up using chi-square tests for independence. Kaplan–Meier curves were used to visualize the occurrence of safety outcomes over time. Results A total of 360 patients at low to intermediate risk of CAD were included, 224 in the CCTA only group, and 136 in the FFR-CT group. During follow-up, 13 MACE occurred in 12 patients, 9 (4.0%) in the CCTA group, and three (2.2%) in the FFR-CT group. Clinical management decisions differed significantly between both groups. After CCTA, 60 patients (26.5%) received optimal medical therapy (OMT) only, 115 (51.3%) invasive coronary angiography (ICA), and 49 (21.9%) single positron emission CT (SPECT). After FFR-CT, 106 patients (77.9%) received OMT only, 27 (19.9%) ICA, and three (2.2%) SPECT ( p  < 0.001 for all three options). The revascularization rate after ICA was similar between groups ( p  = 0.15). However, patients in the CCTA group more often underwent revascularization ( p  = 0.007). Conclusion Addition of FFR-CT to CCTA led to a reduction in (invasive) diagnostic testing and less revascularizations without observed difference in outcomes after 1 year. Key Points • Previous studies have shown that computed tomography–derived fractional flow reserve improves the accuracy of coronary computed tomography angiography without changes in acquisition protocols. • This study shows that use of computed tomography-derived fractional flow reserve as gatekeeper to invasive coronary angiography in patients suspected of stable coronary artery disease leads to less invasive testing and revascularization without observed difference in outcomes after 1 year. • This could lead to a significant reduction in costs, complications and (retrospectively unnecessary) usage of diagnostic testing capacity, and a significant increase in patient satisfaction.

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.

The paper presents the most recent achievements in developing AMADEOS - the first online web-based tool aimed at business process model-driven database design. The preexisting AMADEOS was able only to derive an initial conceptual database model automatically, while other design phases were not supported. The most recent development efforts resulted in the complete coverage of the database design process, from conceptual model to physical database, by using the standard UML notation.

The idea of this paper is the proposal of a low-cost control device based on the concept of IoT, which will have many functionalities integrated. A large number of integrated functionalities make it possible to satisfy a large number of different users. The proposed control device solution could be used both for controlling the operation of devices and machines in industrial plants and for training engineers.

Embedded systems are widely used in different spheres of everyday life. Implementation of web server into these systems enable remote access to processed data. Web server implementation should be suitable for limited resources of these systems. In this paper, web server implementation in system for air parameter monitoring will be presented. This implementation is done using LwIP stack and enables remote access to measurement results within local network. Operation principle of web server and whole system will be discussed.