Early detection of atrial fibrillation plays a crucial role in the timely prevention, diagnosis, and treatment of cardiovascular diseases. This paper proposes two different network architectures for automated atrial fibrillation detection. In the first architecture, a 1D CNN is used as a feature extractor and classifier. In the second hybrid architecture, a 1D CNN is used only as a feature extractor from ECG time series signals that supply a KNN with the most relevant features for further classification. Experimental results showed that the hybrid architecture achieved remarkable results and outperformed a 1D CNN.

Ultrasound images are used in various branches of medicine to detect diseases. The process of obtaining this data is complex due to procedures and legal restrictions, leading to scarce datasets. Different data augmentation techniques can be employed to improve classification performance. This paper shows that augmenting the ultrasound breast cancer images dataset using generative adversarial networks (GANs) increased the classification accuracy compared to the original dataset and compared to the dataset augmented using standard techniques.

The building integrated photovoltaic (BIPV) systems are a popular option for integrating renewable energy sources in the power system, and for users to reduce energy bills. This paper analyzes the performance of inverters in BIPV systems with oversized PV configurations. Oversizing PV systems has become a common practice to optimize energy production, particularly in periods of low sunlight, but it raises concerns about efficiency, power quality, and potential economic implications. Performance analysis is performed on two inverters, one operating under an overloaded regime due to the oversized PV installation and another under normal conditions. Several performance metrics are compared, including efficiency, thermal behavior, THD, and economic factors. The results demonstrate that although oversizing can slightly increase the inverter’s temperature and affect power quality, the efficiency was better for the overloaded inverter, although the investment costs have increased. These results offer practical insights for designing PV systems, showing that oversizing can be beneficial if properly managed.

Random sequential adsorption (RSA) is a broadly used model for irreversible deposition on substrates. Over the last decades, a huge number of works have been published concerning this topic. Here we give a brief review of the results for irreversible deposition on two-dimensional discrete substrates. Depositing objects are randomly and sequentially adsorbed onto the substrate, and they are not allowed to overlap, so the jamming coverage θjam is less than in close packing. The kinetics of the process is described by the time-dependence of the coverage fraction θ(t), and for the discrete substrates, this dependence was found to be of the form: θ(t)=θjam−Ae−t/σ. Another topic of interest is the percolation of the deposit that can occur at a certain coverage. The coverage of the surface is increased through the RSA process up to the percolation threshold when a cluster that extends through the whole system appears. A percolating cluster arises in the system when the opposite edges are connected via some path of nearest neighbor sites occupied by the particles. Studying percolation is of great interest due to its relevance to conductivity in composite materials, flow through porous media, polymerization, the properties of nanomaterials, etc.

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.

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.

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.

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

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.

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.

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)