Deep learning techniques in computer vision (CV) tasks such as object detection, classification, and tracking can be facilitated using predefined markers on those objects. Selecting markers is an objective that can potentially affect the performance of the algorithms used for tracking as the algorithm might swap similar markers more frequently and, therefore, require more training data and training time. Still, the issue of marker selection has not been explored in the literature and seems to be glossed over throughout the process of designing CV solutions. This research considered the effects of symbol selection for 2D-printed markers on the neural network’s performance. The study assessed over 250 ALT code symbols readily available on most consumer PCs and provided a go-to selection for effectively tracking n-objects. To this end, a neural network was trained to classify all the symbols and their augmentations, after which the confusion matrix was analysed to extract the symbols that the network distinguished the most. The results showed that selecting symbols in this way performed better than the random selection and the selection of common symbols. Furthermore, the methodology presented in this paper can easily be applied to a different set of symbols and different neural network architectures.

This study scrutinizes five years of Sarajevo’s Air Quality Index (AQI) data using diverse machine learning models — Fourier autoregressive integrated moving average (Fourier ARIMA), Prophet, and Long short-term memory (LSTM)—to forecast AQI levels. Focusing on various prediction frames, we evaluate model performances and identify optimal strategies for different temporal granularities. Our research unveils subtle insights into each model’s efficacy, shedding light on their strengths and limitations in predicting AQI across varied timeframes. This research presents a robust framework for automatic optimization of AQI predictions, emphasizing the influence of temporal granularity on prediction accuracy, automatically selecting the most efficient models and parameters. These insights hold significant implications for data-driven decision-making in urban air quality control, paving the way for proactive and targeted interventions to improve air quality in Sarajevo and similar urban environments.

In this paper, the multilevel image thresholding methods based on the particle swarm optimization algorithm and different chaotic inertia weight strategies are considered. The performance of each chaotic inertia weight strategy is evaluated using a set of standard test images. Different numbers of image classes are considered. In addition, the paper also considers the multilevel thresholding performance based on commonly employed linear decreasing inertia weight and random inertia weight. All considered multilevel thresholding methods are based on Kapur’s entropy. The experimental results demonstrate that the particle swarm optimization with chaotic inertia weight can be successfully used for multilevel image thresholding.

In this paper, a multilevel thresholding method for image segmentation based on Otsu’s between-class variance and multi-swarm particle swarm optimization algorithm with dynamic learning strategy is presented. The considered multilevel image thresholding method is assessed on various standard test images and for different numbers of thresholds. For each test image and a considered number of thresholds, the mean and the standard deviation of Otsu’s objective function over a number of independent runs are evaluated. The experimental results showcased that this method can be successfully employed in multilevel image thresholding.

The segmentation of pediatric brain MRI into distinct tissues is important for the evaluation of pediatric brain development and the diagnosis of neurological and neurodevelopmental disorders. However, when the used dataset diverges due to various acquisition protocols or biases among patient cohorts, existing deep learning algorithms cannot guarantee correct predictions. Unsupervised domain adaptation approaches have lately shown enormous potential for addressing this problem by limiting the divergence between the distributions of the used datasets. In this paper, we firstly developed a model called 3DUDRSeg, a 3D encoder-decoder for precise autonomous segmentation of pediatric brain tissues. Our proposed 3DUDRSeg model achieved a 98.88% DSC accuracy rate because of the employment of denes blocks and residual units that help relieve the degradation problem during training the network, allowing the performance advantages to be fully utilized. With this approach, our 3DUDRSeg can create more strong features to deal with the wide range of brain tissue variations. Then, we present 3DAdGanSeg, an entropy-based unsupervised domain adaptation framework for segmenting pediatric brain tissues in unannotated datasets via adversarial learning. The suggested model significantly influences the capability to distinguish the borders between tissue classes, with DSC of 85% and HD95 of 1.479 in the case of the dHCP dataset as the source domain and DSC of 81 % and HD95 of 2.061 when using the Schizophrenia Bulletin 2008 dataset as a source domain.

Corticosteroids regulate a number of physiological processes and are synthetic analogs of the natural steroid hormones produced by the adrenal cortex. As drugs, corticosteroids are non-inflammatory and are used for the treatment of plethora of conditions which include arthritis, kidney, skin, lungs or thyroid disorders, for the treatment and relief of symptoms of allergies and symptoms of some gastrointestinal disorders. In addition, glucocorticoids can regulate the effects of inflammatory disorders, including sepsis, autoimmune diseases, and allergies. These conditions are potentially fatal. Consequently, this drug class is among the most commonly prescribed globally. One representative of corticosteroid class of drugs is dexamethasone which is used to treat allergies, adrenal problems, arthritis, asthma, diseases of blood or bone marrow, inflammation, kidney diseases, different types of skin conditions, and episodes of multiple sclerosis. Virulence factors help bacteria colonize the host at the level of the cell. In their nature, these factors are secretory, associated with the membrane or present in the cytosol. Secretory factors allow bacterium to circumvent the host immune response, while membrane factors aid bacterium in adhesion to the host cell. Finally, cytosol factors help bacteria adapt metabolically, physiologically, and morphologically to their changing environment. One such factor is aspartyl proteinase, a protein that degrades other proteins and is a virulence factor in many pathogens playing a role in the host invasion process. Another important virulence factor is the ability to form biofilms, which can render bacteria resistant to antimicrobials. Despite the widespread use of corticosteroids, including dexamethasone, little is known about their possible influence on the expression of virulence factors such as aspartyl proteinase. If such a connection is to exist the use of corticosteroids could elicit pathogenesis in certain microbes. In the here-presented study we wanted to investigate the effects of dexamethasone on the growth, expression of aspartyl proteinase and biofilm formation in three E. coli strains that were previously isolated from patients suffering from urinary tract infection. To this aim, we amended the growth media with 0.5 mg/mL dexamethasone. Bacterial growth was measured over the period of 24 hours and the effect of dexamethasone was established at different time points. Administration of 0.5 mg/mL glucocorticoid drug dexamethasone did not significantly affect bacterial growth. However, it resulted in an increase in concentration of secreted E. coli virulence factor aspartyl proteinase, which increased up to 2.6-fold for some E. coli strains. In addition, we noted the increased biofilm formation in to three out of four studied strains. This study indicates dexamethasone as a possible trigger molecule for the expression of virulence factor aspartyl proteinase in E. coli.

A technical issue with fluid flow heating is the relatively small temperature increase as the fluid passes through the heating surface. The fluid does not spend enough time inside the heating source to significantly raise its temperature, despite the heating source itself experiencing a substantial increase. To address this challenge, the concept of the multiple circular heating of air was developed, forming the basis of this work. Two PTC heaters with longitudinal fins are located within a closed channel inside housing composed of a thermal insulation material. Air flows circularly from one finned surface to another. Analytical modeling and experimental testing were used in the analysis, with established restrictions and boundary conditions. An important outcome of the analysis was the methodology established for the optimization of the geometric and process parameters based on minimizing the transient thermal entropy. In conducting the analytical modeling, the temperature of the PTC heater was assumed to be constant at 150 °C and 200 °C. By removing the restrictions and adjusting the boundary conditions, the established methodology for the analysis and optimization of various thermally transient industrial processes can be applied more widely. The experimental determination of the transient thermal entropy was performed at a much higher air flow rate of 0.005 m3s−1 inside the closed channel. The minimum transient entropy also indicates the optimal time for the opening of the channel, allowing the heated air to exit. The novelty of this work lies in the controlled circular heating of the fluid and the establishment of the minimum transient thermal entropy as an optimization criterion.

The aim of this paper is to investigate the quality of financial statements during the period of crisis. The crisis has a negative effect on the business performance of a company. It is necessary to measure and analyze various aspects of performance and take timely corrective actions in order to achieve business stability. Quality of financial statements can be expressed as one of the business performances. High-quality financial statements are created in an accurate, timely and reliable manner in accordance with all requirements of regulation. Professional accounting regulation determines a large number of obligatory disclosures which have a direct impact on the quality of financial statements. This paper will analyze the quality of financial statements from the aspect of disclosures according to the International Accounting Standard 2 – Inventory (IAS 2). Inventory represents significant assets for production and trade companies. Therefore, the quality of disclosures according to IAS 2 can be significant for adequate business decisions. The research is based on a sample of agricultural and manufacturing companies registered in the AP Vojvodina during the period 2020-2021. The research is based on a descriptive analysis of the quality of disclosures according to the IAS 2 and financial reporting quality index. The results indicate that 41% of the obligatory disclosures are presented in companies reports. Accounting policy for inventories and carrying amount according to inventory classification are identified as disclosures of high quality. On the other hand, disclosure of the write-down of inventories, recognized as an expense for the period is identified at the lower quality level. The research can be of interest for managers, owners, and creators of financial statements in order to improve the quality of financial reporting as a result of disruption during the period of crisis.

Our cross-sectional study demonstrates that there is a high rate of co-trimoxazole induced drug rash, in patients treated for immune related adverse events, with those developing rash appearing to have a reduced survival.

Paediatric and geriatric populations, as well as other special patient populations with swallowing problems, require patient-tai-lored dosage forms. One promising dosage form for these specific populations is orodispersible films. When preparing orodispersible films using sodium carboxymethyl cellulose as the film-forming polymer and glycerine as the plasticizer, it is essential to determine the optimal mixing time and mixing speed of the casting solution to achieve the desired transparency/opacity of the orodispersible films. In this paper, the primary focus is on mixing time and mixing speed, and determining how these two parameters can influence optical characteristics. All tested parameters are supported by FTIR anal - ysis. The obtained results show that either a mixing speed of 7000 rpm on a high-shear mixer for 15 min or a mixing speed of 9000 rpm for 5 min can produce films with optimal optical characteristics.