Objective. The goal of this research was to examine the morphological characteristics and exact anatomical positioning of the greater palatine foramen (GPF), with reference to nearby anatomical landmarks. Material and Method. The research was performed on dry human skulls belonging to the Bosnian and Herzegovina population, using digital vernier calipers. The study began by noting the GPF’s position relative to the maxillary molars, then measuring its distance from the median palatine suture (MPS), the incisive fossa (IF), the posterior border of the hard palate (PBHP), and the posterior nasal spine (PNS). Measurements were conducted bilaterally, and afterwards the data were analyzed using Student’s t-test and Chi-squared test. A statistical significance was set at P<0.05. Results. The statistical analysis revealed that: the distance of the greater palatine foramen (GPF) from the midline is approximately 15.80±1.28 mm on the right side and 15.86±1.19 mm on the left side. The distance of the GPF from the incisive fossa measures about 40.12±2.19 mm on the right side and 40.34±2.08 mm on the left side. The GPF is positioned around 4.00±1.07 mm on the right side and 4.35±1.34 mm on the left side from the posterior border of the hard palate. Lastly, the distance from the GPF to the posterior nasal spine means 17.55±1.99 mm on the right side and 17.61±1.81 mm on the left side in the entire study population. The highest percentage of skulls (73.05%) showed the GPF positioned at the level of the third molar. Conclusion. The findings of this study further emphasize the variations in the location of the greater palatine foramen and underline the importance of thorough preoperative assessment in patients undergoing maxillofacial surgeries and regional block anesthesia.

Accurate variant classification is critical for genetic diagnosis. Variants without clear classification, known as “variants of uncertain significance” (VUS), pose a significant diagnostic challenge. This study examines AlphaMissense performance in variant classification, specifically for VUS. A systematic comparison between AlphaMissense predictions and predictions based on curated evidence according to the ACMG/AMP classification guidelines was conducted for 5845 missense variants in 59 genes associated with representative Mendelian disorders. A framework for quantifying and modeling VUS pathogenicity was used to facilitate comparison. Manual reviewing classified 5845 variants as 4085 VUS, 1576 pathogenic/likely pathogenic, and 184 benign/likely benign. Pathogenicity predictions based on AlphaMissense and ACMG guidelines were concordant for 1887 variants (1352 pathogenic, 132 benign, and 403 VUS/ambiguous). The sensitivity and specificity of AlphaMissense predictions for pathogenicity were 92% and 78%. Moreover, the quantification of VUS evidence and heatmaps weakly correlated with the AlphaMissense score. For VUS without computational evidence, incorporating AlphaMissense changed the VUS quantification for 878 variants, while 56 were reclassified as likely pathogenic. When AlphaMissense replaced existing computational evidence for all VUS, 1709 variants changed quantified criteria while 63 were reclassified as likely pathogenic. Our research suggests that the augmentation of AlphaMissense with empirical evidence may improve performance by incorporating a quantitative framework to aid in VUS classification.

The methodological approach of this cross-sectional confirmatory research was aimed at analyzing the dominant basic motor abilities and situational motor abilities, their general and partial contribution and prediction of the level of success of performance and behaviour in individual components of football as a prerequisite for a more complete explanation and projection of objective indicators of the competence framework of success in the game of football of the researched sample. The starting point was the assumption that this approach enables confirmation or correction of previous proceedings and procedures in the process of selecting young football players for their top achievements in the mature stage of their football career. The specific aim of this work is to determine individual and characteristic typical group profiles of basic motor and specific motor abilities based on different levels of success in different components of the game of football. The research was carried out on a sample of 110 young football players from Sarajevo aged from 12 to 14 years. Seventeen variables were used to assess basic motor skills, 11 variables for the estimation of situationalmotor abilities, and 8 variables for evaluating success in football. The results are a significant incentive for the improvement of selection technology of young football players because they offer an opportunity to model morphological, motor, situational-motoric, diagnostic, prognostic framework with clear indicators requirements and reference values for the ontogeny age of the sample.

In clinical practice, the development of peripheral artery complications in the lower extremities is a significant issue among patients with type 2 diabetes. The progression of stenotic-occlusive disease can be predicted based on the SCORE risk factor assessment and HbA1c levels. Color Doppler findings are crucial for evaluating hemodynamic flow in the arteries of the lower extremities. Aim: To determine HbA1c levels in patients with stenotic-occlusive disease of the lower extremities, correlate risk factor scores and HbA1c levels in the progression of stenotic-occlusive disease, and assess the significance of elevated HbA1c levels in relation to the clinical grade of stenotic-occlusive disease. Patient and methods: The study included 113 patients with type 2 diabetes (52.1%) and 104 non-diabetic patients (47.9%) as the control group, making a total of 217 participants. Both groups were classified as high-risk due to the presence of independent risk factors such as hyperlipidemia, smoking, obesity, and arterial hypertension. When the cumulative SCORE risk factor for the total group of participants (n=217) was analyzed, the results indicated a high level of risk with statistical significance, p<0.0001. Results: Patients with predominantly occlusive changes in the type 2 diabetes group had HbA1c values of 8.25%, which was significantly higher compared to those with stenotic changes, whose HbA1c values were 7.3% (p=0.002). According to the SCORE tables, a value >5% indicates high risk for developing cardiovascular disease, while a SCORE value of 7% was identified as a predictor for disease progression in patients with type 2 diabetes, with high significance (p=0.0001). In the non-diabetic group, lower values of peak systolic velocity (PSV) in the superficial femoral artery (p=0.051) were observed. In the type 2 diabetes group, PSV values in the profunda femoral artery were lower (p=0.053), while significantly lower PSV values were recorded in the anterior tibial artery (p=0.008). Occlusive disease of the lower extremity arteries was present in 89.6% of cases in the type 2 diabetes group, with 90 patients affected, which was significantly higher compared to stenotic disease (p<0.0001). Conclusion: Subjects in the DM2T group with dominant occlusive changes had significantly higher HbA1c values ​​compared to the HbA1c group with dominant stenotic changes p<0,002. The risk factor score for the examined group, DM2T, was 7% (SCORE of high cardiovascular risk), and in the control group, non-diabetics, it was 8%, and both groups are high risk. HbA1c can be a predictor for the development of occlusions on the arteries of the lower extremities in subjects with DM2T.4. DM2T group subjects with occlusive changes had high HbA1c values, ≥8.25.

Background With the advancement of Artificial Intelligence (AI), clinical engineering has witnessed transformative opportunities, enabling predictive maintenance of medical devices, optimization of healthcare workflows, and personalized patient care. Respiratory equipment plays a vital role in modern healthcare, supporting patients with compromised or impaired respiratory capacities. However, ensuring the reliability and safety of these devices is crucial to prevent adverse events and ensure patient well-being. Objective This study aims to explore machine learning techniques to enhance predictive maintenance for mechanical ventilators. Method: The dataset used for this study contains information about 1350 entries of mechanical ventilators, made by 15 different manufacturers and available in 30 distinct models. Different machine learning algorithms, including Logistic Regression, Decision Trees, Random Forest, K-nearest Neighbors, Support Vector Machines, Naive Bayes, and XG Boost are developed and tested in terms of their performance in predicting mechanical ventilator failures. Results The ensemble methods, particularly Random Forest and XGBoost, have proven to be more adept at handling the complexities of the dataset. The Decision Tree and Random Forest models both showed remarkable accuracies of approximately 0.993, while K-Nearest Neighbors (KNN) performed exceptionally with near perfect accuracy. Conclusion Adoption of automated systems based on artificial intelligence will help in overcoming challenges of ensuring quality of MDs that are already being used in healthcare institutions. Implementing machine learning-based predictive maintenance can significantly enhance the reliability of mechanical ventilators in healthcare settings.

This paper aims to investigate the status of alignment and harmonization of corporate reporting in Western Balkans (WB) countries with the European Sustainability Reporting Standards (ESRS). Specifically, the research will focus on understanding the extent to which WB countries have initiated the adoption of ESRS, particularly in the context of the Corporate Sustainability Reporting Directive (CSRD) that mandates its use for companies within the European Union (EU) and its branches. The paper will compare the achieved level of sustainability reporting in Western Balkan countries with other countries located in Europe that are not members of the European Union. Despite the mandatory nature of ESRS for companies within the EU, our preliminary analysis indicates a lack of progress in the alignment and harmonization process among the WB countries. Western Balkan countries are also lagging behind, compared to other non-EU member countries, such as Switzerland and Norway, which have been selected for comparative analysis. The research seeks to uncover the reasons behind this lag and to explore the potential challenges faced by companies in the WB region in implementing these standards. It is crucial to understand the current state of sustainability reporting practices in WB countries and the challenges faced in aligning with ESRS. It will provide valuable insights for policymakers, businesses, and stakeholders on the necessary steps to enhance sustainability reporting practices in the region and foster alignment with international standards.

The flavor puzzles remain among the most compelling open questions in particle physics. The striking hierarchies observed in the masses and mixing of charged fermions define the Standard Model (SM) flavor puzzle, a profound structural enigma pointing to physics beyond the SM. Simultaneously, the absence of deviations from SM predictions in precision measurements of flavor-changing neutral currents imposes severe constraints on new physics at the TeV scale, giving rise to the new physics flavor puzzle. This review provides an overview of a selection of recent advancements in flavor model building, with a particular focus on attempts to address one or both of these puzzles within the quark sector.

As manufacturing technologies advance, the integration of artificial neural networks in machining high-hardness materials and optimization of multi-objective parameters is becoming increasingly prevalent. By employing modeling and optimization strategies during the machining of such materials, manufacturers can improve surface roughness and tool life while minimizing cutting time, tool vibrations, and cutting forces. In this paper, the aim was to analyze the impact of input parameters (cutting speed, feed rate, depth of cut, and insert radius) on surface roughness and cutting forces during the machining of 90MnCrV7 using feed-forward neural network models and SHAP analysis. Afterward, multi-criteria optimization was applied to determine the optimal parameter levels to achieve minimum surface roughness and cutting forces using the modified PSI-TOPSIS method. According to the SHAP analysis, the insert radius has the most significant impact on the surface roughness and passive force, while in the multi-criteria analysis, according to ANOVA results, the insert radius has the most significant impact on all considered outputs. The results show that an insert radius of 0.8 mm, a cutting speed of 260 m/min, a feed rate of 0.08 mm, and a depth of cut of 0.5 mm are the optimal combination of input parameters.

This article aims to elaborate on the theological, philosophical and pedagogical foundations that theoretically frame Islamic education in its various forms (formal, non-formal and informal). First, it highlights the foundations of Islamic education in the normative Islamic tradition and classical Muslim theological thought. Discussion of the philosophy and pedagogy of Islamic education focuses on its fundamental features (specific educational goals, critical/reflective, transformative, integrative, dialogical approach, etc.). Following many initiatives and in view of the needs felt for authentic and independent Islamic education across Europe, this article advocates that the theological, philosophical and pedagogical framework of Islamic education should autonomously shape Islamic educational programmes in Europe and has the potential to fit in European educational settings.

The complex link between COVID‐19 and immunometabolic diseases demonstrates the important interaction between metabolic dysfunction and immunological response during viral infections. Severe COVID‐19, defined by a hyperinflammatory state, is greatly impacted by underlying chronic illnesses aggravating the cytokine storm caused by increased levels of Pro‐inflammatory cytokines. Metabolic reprogramming, including increased glycolysis and altered mitochondrial function, promotes viral replication and stimulates inflammatory cytokine production, contributing to illness severity. Mitochondrial metabolism abnormalities, strongly linked to various systemic illnesses, worsen metabolic dysfunction during and after the pandemic, increasing cardiovascular consequences. Long COVID‐19, defined by chronic inflammation and immune dysregulation, poses continuous problems, highlighting the need for comprehensive therapy solutions that address both immunological and metabolic aspects. Understanding these relationships shows promise for effectively managing COVID‐19 and its long‐term repercussions, which is the focus of this review paper.

The flavor puzzles remain among the most compelling open questions in particle physics. The striking hierarchies observed in the masses and mixing of charged fermions define the Standard Model (SM) flavor puzzle, a profound structural enigma pointing to physics beyond the SM. Simultaneously, the absence of deviations from SM predictions in precision measurements of flavor-changing neutral currents imposes severe constraints on new physics at the TeV scale, giving rise to the new physics flavor puzzle. This review provides an overview of a selection of recent advancements in flavor model building, with a particular focus on attempts to address one or both of these puzzles within the quark sector.

Objective. Previous studies have demonstrated that the speech reception threshold (SRT) can be estimated using scalp electroencephalography (EEG), referred to as SRTneuro. The present study assesses the feasibility of using ear-EEG, which allows for discreet measurement of neural activity from in and around the ear, to estimate the SRTneuro. Approach. Twenty young normal-hearing participants listened to audiobook excerpts at varying signal-to-noise ratios (SNRs) whilst wearing a 66-channel EEG cap and 12 ear-EEG electrodes. A linear decoder was trained on different electrode configurations to estimate the envelope of the audio excerpts from the EEG recordings. The reconstruction accuracy was determined by calculating the Pearson’s correlation between the actual and the estimated envelope. A sigmoid function was then fitted to the reconstruction-accuracy-vs-SNR data points, with the midpoint of the sigmoid serving as the SRTneuro estimate for each participant. Main results. Using only in-ear electrodes, the estimated SRTneuro was within 3 dB of the behaviorally measured SRT (SRTbeh) for 6 out of 20 participants (30%). With electrodes placed both in and around the ear, the SRTneuro was within 3 dB of the SRTbeh for 19 out of 20 participants (95%) and thus on par with the reference estimate obtained from full-scalp EEG. Using only electrodes in and around the ear from the right side of the head, the SRTneuro remained within 3 dB of the SRTbeh for 19 out of 20 participants. Significance. These findings suggest that the SRTneuro can be reliably estimated using ear-EEG, especially when combining in-ear electrodes and around-the-ear electrodes. Such an estimate can be highly useful e.g. for continuously adjusting noise-reduction algorithms in hearing aids or for logging the SRT in the user’s natural environment.

Accurate ear counting is essential for determining wheat yield, but traditional manual methods are labour-intensive and time-consuming. This study introduces an innovative approach by developing an automatic ear-counting system that leverages machine learning techniques applied to high-resolution images captured by unmanned aerial vehicles (UAVs). Drone-based images were captured during the late growth stage of wheat across 15 fields in Bosnia and Herzegovina. The images, processed to a resolution of 1024 × 1024 pixels, were manually annotated with regions of interest (ROIs) containing wheat ears. A dataset consisting of 556 high-resolution images was compiled, and advanced models including Faster R-CNN, YOLOv8, and RT-DETR were utilised for ear detection. The study found that although lower-quality images had a minor effect on detection accuracy, they did not significantly hinder the overall performance of the models. This research demonstrates the potential of digital technologies, particularly machine learning and UAVs, in transforming traditional agricultural practices. The novel application of automated ear counting via machine learning provides a scalable, efficient solution for yield prediction, enhancing sustainability and competitiveness in agriculture.