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.

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 %.

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.

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.

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.

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.

Comparing portfolio performance is complex due to the fact that each model is dominant in its own risk space. Since there is no single dominant performance measure, the research problem is how to incorporate several different measures into a performance evaluation model that allows portfolios to be ranked. In this regard, the objective of this study was to develop a new comprehensive method for comparing portfolio performance based on multiple-criteria decision-making (MCDM). This paper proposes an integrated approach for stock market decision making that combines the Analytic Hierarchy Process (AHP) and the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE), which allow hierarchical evaluation of a finite number of alternatives according to different criteria. This hybrid approach is especially advantageous, utilizing the strengths of both individual methods. AHP enables the decomposition of a complex problem into its constituent parts and the determination of weights for criteria, while the PROMETHEE method allows the investor to determine the preference function, complete ranking, and analysis of the robustness of the results. For the MCDM model in this study, different dimensions of performance measures are considered criteria: return measures, risk measures, stability measures, and predictability measures. The methodology has been applied in comparing real portfolios selected on the basis of different risk measures. For this purpose, weekly return data were used for a sample of stocks that are components of the STOXX Europe 600 Index for the period 2000–2020. In addition, a sensitivity analysis is performed to investigate the strength of the results of this method. It suggests that the simultaneous consideration of different performance measures and the investor’s attitude towards the importance of these measures are notably important in the portfolio efficiency estimation process.

Citizens and policy institutions increasingly express their concerns regarding the emerging challenges in the context of Artificial Intelligence (AI) and have concrete demands for the protection of human rights. In parallel, studies in the field of AI and Human-Robot Interaction (HRI) indicate the impact of social robots on children's development. We conducted a systematic review based on UNICEF's AI Policy Guidance to map the landscape of research on social robots and children's rights. We used the PRISMA method and identified N=37 papers that address one of the rights, which we then annotated to indicate tendencies and areas of alignment and misalignment with the UNICEF guidance. Our findings reveal that although the field of HRI is looking at specific rights, with a focus on inclusion, some of the rights have been under-researched. Furthermore, we observed a misalignment between HRI and UNICEF regarding the terminology. With this paper, we hope to bring awareness to the field of HRI regarding children's rights and to highlight directions for alignment among research, societal needs, and policy.

Robots could support older adults' well-being by engaging them in meaningful conversations, specifically to reflect on, support, and improve different aspects of their well-being. We implemented a system on a QT social robot to conduct short autonomous conversations with older adults, to help understand what brings them feelings of joy and meaning in life. We evaluated the system with written surveys and observations of 12 participants including older adults, caregivers, and dementia care staff. From this, we saw the need to improve user experience through personalized interaction that better support older adults as they talk about well-being. Improving the interactions will involve improving the conversation flow, detecting emotions and nonverbal cues, and natural language processing to extract topics around well-being.

The use of socially assistive robots is able to alleviate some depression symptoms, according to existing research. However, due to comorbidities that often accompany depression and the unique experiences of each individual, it is necessary to get a better understanding of how SARs should be personalized. Through 10 hourlong workshops with 10 individuals living with depression, we explored the customization of a zoomorphic SAR for adults with depression. By using the SAR Therabot? as a base platform, participants designed their own unique covering for the robot, and discussed desired robot behaviors and privacy concerns around data collection. Though the physical designs of the robots varied greatly, participants expressed common themes regarding their preference for a soft touchable exterior, comfort with sharing data with their therapists, and interest in the robot producing more realistic sounds and movements, among other design features.