Background Healthcare institutions throughout the world rely on medical devices to provide their services reliably and effectively. However, medical devices can, and do sometimes fail. These failures pose significant risk to patients. Objective One way to address these issues is through the use of artificial intelligence for the detection of medical device failure. This goal of this study was to develop automated systems utilising machine learning algorithms to predict patient monitor performance and potential failures based on data collected during regular safety and performance inspections. Methods The system developed in this study utilised machine learning techniques as its core. Throughout the study four algorithms were utilised. These algorithms include Decision Tree, Random Forest, Linear Regression and Support Vector Machines. Results Final results showed that Random Forest algorithms had the best performance on various metrics among the four developed models. It achieved accuracy of 94% and precision and recall of 70% and 93% respectively. Conclusion This study shows that use of systems like the one developed in this study have the potential to improve management and maintenance of medical devices.

Background Analysis of data from incident registries such as MAUDE has identified the need to improve surveillance and maintenance strategies for infusion pumps to enhance patient and healthcare staff safety. Objective The ultimate goal is to enhance infusion pump management strategies in healthcare facilities, thus transforming the current reactive approach to infusion pump management into a proactive and predictive one. Method: This study utilized real data collected from 2015 to 2021 through the inspection of infusion pumps in Bosnia and Herzegovina. Inspections were conducted by the national laboratory in accordance with the Legal Metrology Framework, accredited to ISO 17020 standard. Out of 988 samples, 790 were used for model training, while 198 samples were set aside for validation (20% of the dataset). Various machine learning algorithms for binary classification of samples (pass/fail status) were considered, including Logistic Regression, Decision Tree, Random Forest, Naive Bayes, and Support Vector Machine. These algorithms were chosen for their ability to handle large datasets and potential for high prediction accuracy. Results Through detailed analysis of the achieved results, it was found that all applied machine learning methods yielded satisfactory results, with accuracy ranging from 0.98% to 1.0%, precision from 0.99% to 1%, sensitivity from 0.98% to 1.0%, and specificity from 0.87% to 1.0%. However, Decision Tree and Random Forest methods proved to be the best, both due to their maximum achieved values of accuracy, precision, sensitivity, and specificity, and due to result interpretability. Conclusion It has been established that machine learning methods are capable of identifying potential issues before they become critical, thus playing a crucial role in predicting the performance of infusion pumps, potentially enhancing the safety, reliability, and efficiency of healthcare delivery. Further research is needed to explore the potential application of machine learning algorithms in various healthcare domains and to address practical issues related to the implementation of these algorithms in real clinical settings.

The project phase is where the life-cycle of a building starts. The best decisions are made during the design or pre-project step. In terms of both economic resources and time, changes to specific design decisions made at this step are inexpensive compared to subsequent steps of architectural planning, not to mention the course of the construction's operation itself. The choices made during the design phase determine to a large extent whether the architectural design decisions of a building are achieved, whether the building and site can be used appropriately, and whether the project is economically viable. With BIM, building spatial planning is possible. As a result, architects can evaluate the proposed structure, its impact on the ecology, and the ecology's impact on the structure more comprehensively and at an earlier stage. This research proposes an energy modeling approach for the BIM-based spatial planning phase of a construction. The proposed method will result in an energy model for specific sites and building resolutions when utilized to create a spatial modelling for a construction. The energy model can then be used for new architectural creations. In this study, 36 different alternative scenarios were designed in terms of the rate of construction height to construction spacing, orientation factor, and form factor. With the help of BIM and GBS softwares, the energy consumption values of the alternative scenarios in cooling and heating load conditions were compared, and the alternative scenario with the minimum energy consumption was tried to be determined with spatial planning parameters.Ključne riječi

Nanotechnology has seen significant growth in the past few decades, with the use of nanomaterials reaching a wide scale. Given that antimicrobial resistance is peaking, nanotechnology holds distinct potential in this area. This review discusses recent applications of metal and metal oxide nanoparticles as antibacterial, antifungal, and antiviral agents, particularly focusing on their topical applications and their role in chronic wound therapy. We explore their use in various forms, including coated, encapsulated, and incorporated in hydrogels or as complexes, proposing them as topical antimicrobials with promising properties. Some studies have shown that metal and metal oxide nanoparticles can exhibit cytotoxic and genotoxic effects, while others have found no such properties. These effects depend on factors such as nanoparticle size, shape, concentration, and other characteristics. It is essential to establish the dose or concentration associated with potential toxic effects and to investigate the severity of these effects to determine a threshold below which metal or metal oxide nanoparticles will not produce negative outcomes. Therefore, further research should focus on safety assessments, ensuring that metal and metal oxide nanoparticles can be safely used as therapeutics in biomedical sciences.

Single-beam echo sounders have gained popularity for various applications due to their compact dimensions, ease of use, and cost-effectiveness. The question that often arises among the users is whether these devices can fulfill the necessary accuracy requirements. This paper concentrates on assessing the accuracy that can be achieved using a single-beam echo sounder. An accuracy assessment was performed by comparing the depths derived from the 3D model created from the single-beam echo sounder data to those obtained through more accurate and independent method (tacheometric surveying) in the test area. Accurate depth determination was achieved through trigonometric leveling, employing a specific methodology that allows for precise depth measurements up to 4.5 meters. The assessment results were compared to the vertical accuracy requirements for surveying and mapping in shallow waters, recommended by the International Hydrographic Organization. The results indicate that, with a 95% probability, the depths determined by the single-beam echo sounder meet the total vertical uncertainty (TVU) requirements specified by the S-44 standard for Order 1a survey.

Long-term use of topical prostaglandins might initiate chronic conjunctival inflammation, leading to poor outcomes of glaucoma surgery. The aim of this study was to evaluate the immunoexpression pattern of HSP70, CTGF, SNAIL, aSMA, cMYB, and HIFa in the conjunctiva, episclera, and deep sclera in patients with glaucoma undergoing deep sclerectomy in order to establish an association between staining intensities and prostaglandin F2 (PGF2) treatment. Double immunofluorescence (HSP70, CTGF, SNAIL, aSMA, cMYB, and HIFa) was performed on conjunctiva, episclera, and deep sclera samples, which were obtained from 23 patients treated with PGF2 and 8 patients without PGF2 treatment. When comparing the ocular tissues of patients regarding treatment with PGF2 analogs, we found a significant increase in the immunoexpression of HSP70 in the conjunctival epithelium of patients treated with PGF2 analogs compared to those without PGF2 treatment. These patients also had an increase in SNAIL immunoexpression and a decrease in aSMA immunoexpression in the deep sclera. There were no significant differences in HIFa, CTGF, or cMYB immunoexpression levels between the two groups. Further research into the regulation of these factors in ocular tissues could lead to the development of potential novel therapeutic approaches in glaucoma management.

Background: The aim of this research is to investigate the associations between physical activity and mental health parameters (depression, anxiety, and stress) among women who have recovered from COVID-19; Methods: This research involved two measurements: the initial test, conducted 2-to-4 weeks post-COVID infection, and the final test, performed 14-to-16 weeks after the virus’s activity. The sample consisted of women (n = 190) aged 20 to 60 (47.60 ± 11.1, 47.60 ± 11.1, mean ± Std.Dev.) who were infected with COVID-19. To assess the level of physical activity, a longer version of the IPAQ questionnaire was used. Self-assessment of mental health was determined by a longer version of the DASS questionnaire; Results: The t-test analysis revealed significant differences in mental health and physical activity levels between the initial and final measurements. After three months, subjects showed lower mental health scores (indicating improvement) and higher Metabolic Equivalent of Task (MET) values across all physical activity domains, with moderate physical activity showing the greatest increase. The regression analysis showed that at the initial measurement, there was no statistically significant association of physical activity with mental health parameters. Three months after the initial measurement, regression analysis showed that there was a statistically significant association of physical activity with anxiety (F = 3.97; p = 0.000) and depression (F = 3.34; p = 0.001) but not with stress (F = 1.67; p = 0.106); Conclusions: This research revealed that higher levels of physical activity improved mental health in post-COVID-19 women, with varying effects on anxiety and depression depending on the activity domain.

Abstract We consider finite portions of the regular hexagonal lattice and count the ways of dividing narrow strips of such a lattice into a given number of parts. We prove that such divisions are enumerated by the odd-indexed Fibonacci numbers, thus providing a new combinatorial interpretation of that sequence. We offer three different proofs of this result. Consequently, we obtain a new combinatorial proof of a well-known Fibonacci-related identity. At the end of the paper, we interpret our results in the context of graph compositions and indicate some possible directions for further research.

Electrochemical energy storage and conversion technologies, which include fuel cells, electrolyzers, batteries, photoelectrochemical devices are at the forefront of the transition to a sustainable future. Although they have all been in use for more than half a century, they are far from reaching their full potential as defined by the laws of thermodynamics. Their performance rests almost entirely on the electrochemical interface - the boundary between the electronic conductor (electrode) and the ionic conductor (electrolyte). The desire of both phases to reduce the surface energy as well as the appearance of electrochemical potential across the interface can manifest itself as the formation of unique (near)surface atom arrangements (e.g. surface relaxation or reconstruction), as significant differences in electrode composition close to the surface (e.g. segregation profile), via substrate-adsorbate covalent and non-covalent interactions, via formation of a passive film as well as ordering of solvent and/or electrolyte molecules several nm away from the surface. This extremely complex and sensitive "interfacial bridge", is a consequence of inherent incompatibility of two materials, brought into contact, and is very hard to control. However, to control it means to control the energy efficiency, power density, durability and safety – the most important metrics of any energy conversion and storage device. In this presentation we will discuss, how the chemical nature of non-covalently and covalently [1,2] adsorbed species as well as thicker passive films and their morphology at the electrochemical interface affect the individual terms of the common rate equation [1], including the free energy of adsorbed intermediates and adsorbed spectators, mass transport, availability of active sites and electronic and ionic resistivity for common electrocatalytic reactions in acid and alkaline aqueous as well as in non-aqueous media on a plethora of metal electrodes (Pt, Ir, Au, Ni, Cu) as well as carbon. We will draw parallels between HER, OER, HOR and ORR in electrolyzers [1], fuel cells [2] and Li-ion batteries [3,4]. The interphase properties will be discussed through the lens of deviations of modified electrode properties from its intrinsic properties. Examples of artificially modified interfaces [5,6] will be given to demonstrate our ability to tailor their activity, stability and selectivity to our liking. [1] Strmcnik, D. Lopes, P.P., Genorio, B., et al. Design principles for hydrogen evolution reaction catalyst materials, Nano Energy, 29, 29-36 (2016) [2] Strmcnik, D., Uchimura, M., Wang, C. et al. Improving the hydrogen oxidation reaction rate by promotion of hydroxyl adsorption. Nature Chem 5, 300–306 (2013) [3] Strmcnik, D., Castelli, I.E., Connell, J.G. et al. Electrocatalytic transformation of HF impurity to H2 and LiF in lithium-ion batteries. Nat Catal 1, 255–262 (2018) [4] Martins, M., Haering, D., Connell, J.G., et al. Role of Catalytic Conversions of Ethylene Carbonate, Water, and HF in Forming the Solid-Electrolyte Interphase of Li-Ion BatteriesACS Catalysis 13, 9289-9301 (2023) [5] Zorko, M. Martins, P.F.B.D., Connell, J.G. et al. Improved Rate for the Oxygen Reduction Reaction in a Sulfuric Acid Electrolyte using a Pt(111) Surface Modified with Melamine, ACS Applied Materials & Interfaces 13, 3369-3376 (2021) [6] Strmcnik, D., Escudero-Escribano, M., Kodama, K. et al. Enhanced electrocatalysis of the oxygen reduction reaction based on patterning of platinum surfaces with cyanide. Nature Chem 2, 880–885 (2010)

Traditional design strategies for catalytic materials for HER rely on the volcano plot paradigm, where the metal-Had bond energy is used as a single activity-descriptor. However, the use of metal-Had energetics alone completely fails to predict the behavior of HER in alkaline electrolytes. We have persistently drawn attention to the importance of kinetic synergy (bifunctional nature) of the HER in alkaline electrolytes, where both the beneficial OHad–M and Had–M energetics are necessary for achieving a highly effective catalyst [1-4]. This is especially evident on metal surfaces, decorated with small clusters of Ni(OH)2, where an order of magnitude increase in catalytic activity for the HER can most often be achieved [5]. Furthermore, we have stressed the importance of spectator species in any satisfactory description of most common electrocatalytic reactions, with HER being no exception [6,7]. Nonetheless, both topics are still a subject of many academic discussions up to this day. In this presentation, we will focus on the electrochemical behavior of Ni in alkaline electrolyte. We argue that the experimentally obtained “intrinsic” HER activity values reported in the available literature are misleading possibly due to two main challenges: i) Ni has very complex surface chemistry (because of the metal-hydride formation during the HER and/or the partial coverage by various (hydr)oxide species, even in the HER potential region), and ii) analysis of the material intrinsic properties has most often not been conducted on well-defined systems. By overcoming these two challenges, we will show the role of the individual Ni surface species in the kinetics of the HER, identify the active sites involved in the reaction, and present strategies for controlling and manipulating the electrochemical interface to enhance the efficiency of Ni-based material for HER. Finally, we again highlight the importance of 2 major factors controlling the rate of HER in alkaline solutions on Ni-based catalysts: the availability of active sites on Ni electrode surface [the 1−Θad term] and the energetics of the activated water complex [the ΔG0# (H2O) term]. Through meticulous experimental design, we were able to isolate and examine these variables, revealing their distinct influence on reaction kinetics. We will discuss our findings in the broader context of the volcano plot for HER. References: [1] Stamenkovic, V.R., Strmcnik, D., Lopes, P.P. and Markovic, N.M., Nature Materials, 16 (2017) [2] Subbaraman, R., Tripkovic, D., Strmcnik, D., Chang, K.C., Uchimura, M., Paulikas, A.P., Stamenkovic, V.R., Markovic, N.M., Science, 334 (2011) [3] Subbaraman, R., Tripkovic, D., Chang, K.C., Strmcnik, D., Paulikas, A.P., Hirunsit, P., Chan, M., Greeley, J., Stamenkovic, V.R., Markovic, N.M., Nature Materials, 11 (2012) [4] Strmcnik, D., Lopes, P. P., Genorio, B., Stamenkovic, V. R. & Markovic, N. M., Nano Energy, 29 (2016) [5] Danilovic, N., Subbaraman, R., Strmcnik, D., Chang, K.C., Paulikas, A.P., Stamenkovic, V.R., Markovic, N.M., Angewandte Chemie International Edition, 124 (2012) [6] Stamenkovic, V.R., Fowler, B., Mun, B.S., Wang, G., Ross, P.N., Lucas, C.A., Markovic, N.M., Science, 315 (2007) [7] Strmcnik, D., Uchimura, M., Wang, C., Subbaraman, R., Danilovic, N., Van Der Vliet, D., Paulikas, A.P., Stamenkovic, V.R. and Markovic, N.M., 2013, Nature Chemistry, 5 (2013)

Time series classification is a relevant step supporting decision-making processes in various domains, and deep neural models have shown promising performance. Despite significant advancements in deep learning, the theoretical understanding of how and why complex architectures function remains limited, prompting the need for more interpretable models. Recently, the Kolmogorov-Arnold Networks (KANs) have been proposed as a more interpretable alternative. While KAN-related research is significantly rising, to date, the study of KAN architectures for time series classification has been limited. In this paper, we aim to conduct a comprehensive and robust exploration of the KAN architecture for time series classification on the UCR benchmark. More specifically, we look at a) how reference architectures for forecasting transfer to classification, at the b) hyperparameter and implementation influence on the classification performance in view of finding the one that performs best on the selected benchmark, the c) complexity trade-offs and d) interpretability advantages. Our results show that (1) Efficient KAN outperforms MLP in performance and computational efficiency, showcasing its suitability for tasks classification tasks. (2) Efficient KAN is more stable than KAN across grid sizes, depths, and layer configurations, particularly with lower learning rates. (3) KAN maintains competitive accuracy compared to state-of-the-art models like HIVE-COTE2, with smaller architectures and faster training times, supporting its balance of performance and transparency. (4) The interpretability of the KAN model aligns with findings from SHAP analysis, reinforcing its capacity for transparent decision-making.

Hearing impairment alters the sound input received by the human auditory system, reducing speech comprehension in noisy multi-talker auditory scenes. Despite such difficulties, neural signals were shown to encode the attended speech envelope more reliably than the envelope of ignored sounds, reflecting the intention of listeners with hearing impairment (HI). This result raises an important question: What speech-processing stage could reflect the difficulty in attentional selection, if not envelope tracking? Here, we use scalp electroencephalography (EEG) to test the hypothesis that the neural encoding of phonological information (i.e., phonetic boundaries and phonological categories) is affected by HI. In a cocktail-party scenario, such phonological difficulty might be reflected in an overrepresentation of phonological information for both attended and ignored speech sounds, with detrimental effects on the ability to effectively focus on the speaker of interest. To investigate this question, we carried out a re-analysis of an existing dataset where EEG signals were recorded as participants with HI, fitted with hearing aids, attended to one speaker (target) while ignoring a competing speaker (masker) and spatialised multi-talker background noise. Multivariate temporal response function (TRF) analyses indicated a stronger phonological information encoding for target than masker speech streams. Follow-up analyses aimed at disentangling the encoding of phonological categories and phonetic boundaries (phoneme onsets) revealed that neural signals encoded the phoneme onsets for both target and masker streams, in contrast with previously published findings with normal hearing (NH) participants and in line with our hypothesis that speech comprehension difficulties emerge due to a robust phonological encoding of both target and masker. Finally, the neural encoding of phoneme-onsets was stronger for the masker speech, pointing to a possible neural basis for the higher distractibility experienced by individuals with HI.

BACKGROUND The development and progression of prostate cancer are multistep processes involving several growth factors, hormones, and cytokines. This study aimed to measure the serum concentrations of different cytokines and determine their correlation with prostate-specific antigen (PSA) levels and disease grade in patients with prostate adenocarcinoma. MATERIAL AND METHODS This cross-sectional study was conducted from March 2023 to March 2024 at the Clinic of Oncology of the University Hospital Center in Mostar, Bosnia and Herzegovina. Altogether, 50 male patients with prostate adenocarcinoma were included, of whom 28 had no proven metastases (PC group) and 22 had metastatic disease (MPC group). Serum concentrations of total (tPSA), free (fPSA), and complexed (cPSA) PSA were determined using a chemiluminescent microparticle immunoassay, whereas serum concentrations of cytokines were measured using a flow cytometry bead-based assay. RESULTS The MPC group had higher serum tPSA, fPSA, and cPSA levels than the PC group. The PC group had significantly higher serum levels of monocyte chemotactic protein (MCP)-1 than the MPC group (P=0.008). In the PC group, serum levels of interleukin (IL)-10 significantly correlated with cPSA. In the MPC group, serum concentrations of IL-1ß, tumor necrosis factor (TNF)-alpha, and IL-23 significantly correlated with disease grade. CONCLUSIONS Our study emphasizes the importance of MCP-1 in the development of prostate cancer, while IL-10 was the only cytokine whose serum level significantly correlated with cPSA. Serum concentrations of IL-1ß, TNF-alpha, and IL-23 may serve as potential biomarkers for disease grade.

The paper explains the phenomenon of creativity through various implicit and explicit definitions and three theories of education through the most important characteristics of the theories and their most significant representatives. With the aim of better familiarization with the concept of creativity, the „position" of creativity in the critical-rationalist empirical, critical and spiritual theory of education is explained, and the understanding of creativity through the areas of the aforementioned theories of education is presented. The paper presents the relationship between pedagogy and creativity and highlights the importance of creativity in the field of teaching work. The need for creativity in educational work and the importance of developing creativity in that context and in the teaching of foreign languages were especially discussed. Given that educational theories talk about the empirical justification of pedagogical facts through critical-rationalist empirical theory, then about the developmental aspects of society and the way society influences scientific knowledge through critical theory and the understanding of human activity through spiritual scientific theory, it is justified to talk about the representation of creativity in the areas of activity of all three educational theories.