Background: Thanks to advancements in neonatal medicine, perinatal morbidity has been significantly reduced, but the number of high-risk neonates continues to rise. Efforts to predict neurodevelopmental outcomes at an early age remain limited. The aim of this study was to analyze perinatal predictors of neurodevelopmental outcomes in high-risk neonates. Methods: A prospective, longitudinal two-year study was conducted at the Pediatric Clinic of the University Clinical Center in Tuzla. The study included 151 neonates, with 99 in the test group (with known perinatal risk factors) and 52 in the control group (without risk factors). Early neurodevelopment was assessed using the Alberta Infant Motor Scale (AIMS). Standard statistical methods were applied for data processing. The study was approved by the Institutional Ethics Committee. Results: Of the 151 neonates observed, 108 (71.5%) had normal neurodevelopment at 18 months, 29 (19.2%) had mild disorders, and 14 (9.3%) had developmental delays. In the group with suboptimal neurodevelopment, significantly more twin pregnancies, health problems during pregnancy, unnatural births, artificial fertilization, and pregnancy complications were recorded. In neonates, there were significantly more premature births, hypoxic-ischemic encephalopathy, and intracranial hemorrhages. Significant correlations were found between the mother's age and parity and delayed neurodevelopment. Additionally, correlations were found between birth weight, gestational age, Apgar score, length of hospitalization, and NICU stay with neurodevelopmental delay. Gestational age and the Apgar score at 1 minute showed significant negative predictive value for neurodevelopmental delay. Conclusion: Prematurity and perinatal asphyxia remain the greatest risks for adverse neurodevelopmental outcomes in neonates. These factors should be the focus of continued medical research and clinical practice. Neonates at the highest risk of developmental delay and their families should be prioritized for early identification, long-term follow-up, and timely interventions.

This study empirically explores short run and long run causality between institutional financial inclusion and income inequality in 22 members of European Union (EU) divided in two subpanels: Old EU members and New EU Member states (NMS). A panel VECM (PVECM) approach is utilized to observe the dynamic causal relationship between financial inclusions on income inequality when control the effect of economic development on inequality. The current level of financial inclusion in the old EU members only through expansion ATMs services lead to decrease income inequality in long run while it contributes through expansion commercial bank branches in short run. On the contrary, the study finds weak and subdued effect financial inclusion on income inequality in the NMS countries in long run, while only expansion commercial bank branches lead to decrease income inequality in short run. The results show that financial inclusion measured by commercial bank branches contributes to more equal income distribution for both regions, only in the short run. In fact, deepening institutional financial inclusions by increasing availability and diversify of specialized financial products and ATM services are still needed to address impediments to financial inclusion, especially in the NMS.

One of the tasks of mathematics education is to develop students' logical thinking. Logical problems that require a certain level of sharpness, ingenuity, and rational reasoning present a good challenge that can further motivate students to engage with mathematics. The progress of science and technology has led to the emergence of numerous tools that can be used in mathematics education. With the advent of artificial intelligence, these tools got a more active role as communication with users reached a new user-friendly dimension resembling human-to-human interaction. The unpredictability of the flow and the speed of development of artificial intelligence raises the question of whether it will ever reach or surpass the level of human sharpness and ingenuity. This paper illustrates inaccurate and partially accurate reasoning of artificial intelligence in solving logical problems.

The Carbon Border Adjustment Mechanism (CBAM) is an import fee levied by a region, i.e., the European Union (EU), that taxes carbon on goods produced in countries that do not tax carbon. This EU climate neutrality support mechanism, which should be implemented by 2050, has caused some concerns. For example, it could reduce the export of the EU’s trading partners, especially those countries largely dependent on exporting energy-intensive goods and materials to the EU. Least developed countries, due to their high risk of vulnerability and high exposure, could face particularly pronounced adverse effects from the CBAM’s introduction. Therefore, this article aims to analyze how the introduction of the CBAM will affect the EU’s external trade partners, with particular attention to its potential consequences for selected economies. Most of the literature related to the introduction of the CBAM focuses on the consequences for EU countries. However, this mechanism, which aims to decrease CO2 emissions and encourage a low-carbon transition, could disproportionately affect some countries outside the EU. That is why the article uses data on the exposure of selected non-EU countries to the CBAM, utilizing data on the export of the CBAM-affected products to the EU and CO2 emission intensity. The analysis encompasses five regions and 59 countries, using data from the year 2019. The rationale for using 2019 data is to avoid the effects of global shocks in recent years, such as the coronavirus pandemic and Russia’s invasion of Ukraine. The analysis results reveal that the exposure of regions and countries varies based on the strength on their trade relations with the EU, leading to different trade impacts from the CBAM. The lowest exposure is observed in the regions of the Americas and Australia. This article provides valuable insights to policymakers and entrepreneurs in navigating the challenges and opportunities arising from the interlinkage of environmental policies and global trade dynamism. It can help facilitate decision-making related to participation in foreign trade involving products with a higher carbon emissions.

Red mud, byproduct of the aluminum industry, poses a significant environmental problem due to its chemical composition and the large quantities generated. This study explores the potential for its valorization through a multi-step process that includes reduction, leaching with acid solution, purification, and ultrasonic spray pyrolysis (USP). In the first step, red mud undergoes carbothermal reduction, during which metal oxides are transformed into metal phase and slag. In the second step, the slag is subjected to leaching under various conditions, including changes in pressure, temperature, and acid concentration, which allows the recovery of titanium in the form of titanium oxysulfate and other metals. Finally, after purifying the solution using precipitation or solvent extraction, ultrasonic spray pyrolysis is used to synthesize titanium dioxide (TiO₂) nanopowders from titanium oxysulfate (TiOSO₄). This integrated approach not only reduces waste but also produces high-value materials with broad industrial applications.

Introduction: Metacognition, which refers to the ability to oversee and regulate one's cognitive activities, plays a crucial role in medical education. Evaluating metacognitive awareness is particularly important for university students, as it impacts their learning strategies and academic performance. The objective of this study was to examine the psychometric characteristics of the Serbian adaptation of the Metacognitive Awareness Inventory (MAI), with an emphasis on its construct validity and internal consistency. Materials and methods: This was a cross-sectional study conducted at the Faculty of Medicine University of Belgrade, on third-year medical students during October 2024. The psychometric properties of the Serbian version of the MAI were evaluated by assessing its factorial structure and internal consistency. The MAI consists of 52 items, comprising two primary components: 'Knowledge of Cognition' and 'Regulation of Cognition'. The subcomponent of "knowledge of cognition" were categorized into declarative knowledge, procedural knowledge, and conditional knowledge, whereas "regulation of cognition" subcomponent were classified into planning, information management strategies, comprehension monitoring, debugging, and evaluation. Results: A total of 426 medical students were included in the study. The mean age of the respondents was 21.3 ± 1.4 years, and the majority were females (68.5%). The mean score of the scale was 199.0. The internal consistency analysis of the Serbian version of the MAI questionnaire yielded a Cronbach's a of 0.94, and a McDonald's o of 0.95 for the entire scale, which indicates excellent scale reliability. The Serbian version of the MAI questionnaire was validated using confirmatory factor analysis, supporting both the hypothesized eight-factor and two-factor structures. The eight-factor model showed good fit indices (IFI = 0.908, CFI = 0.905, RMSEA = 0.042), with statistically significant factor loadings (p < 0.05). Similarly, the two-factor model demonstrated adequate fit (IFI = 0.921, CFI = 0.918, RMSEA = 0.039), with all loadings also significant (p < 0.05). Conclusion: The present study revealed the Serbian version of MAI as reliable and valid tool for identifying medical students' metaconginive awareness.