The aim of the present study was to examine rapid automatized naming(RAN)abilities in primary school students and to construct percentile norms for the applied tasks. A total of 480 students participated in the study. The assessment included RANtasks with objects, numbers, letters,and colors.Results showed that with increasing chronological age, naming speed improved. Percentile norms for the applied tasks are presented separately for each grade, from the secondto fifth.Keywords:dyslexia, rapid automatized naming, children.

Maleic anhydride is a key intermediate in the chemical industry, predominantly produced through the partial oxidation of n-butane over vanadium-phosphorus-oxide (VPO) catalysts. This reaction is accompanied by side reactions that lead to the formation of undesired by-products, primarily CO and CO₂. In this work, a previously developed mathematical model of a fixed-bed tubular reactor was extended to include a catalyst activity function accounting for catalyst deactivation, and the kinetic parameters were optimized using experimental data from an industrial reactor at Koksara d.o.o. Lukavac. The model describes the partial oxidation of n-butane to maleic anhydride through multiple reactions, with reaction rates expressed as functions of temperature, partial pressures, and catalyst activity. Numerical simulations were performed using MATLAB, employing a nonlinear least-squares solver to minimize the deviation between the predicted and measured temperature profiles along the reactor. The validated model showed good agreement with experimental data, demonstrating its capability to accurately simulate reactor behavior under typical industrial conditions. Parametric studies were conducted to analyze the effects of inlet n-butane and oxygen flow rates, reaction mixture temperature, and pressure on the formation of CO and CO₂. The results indicate that by-product formation is strongly influenced by the oxygen/n-butane ratio, temperature, pressure, and the catalyst oxidation state. Higher oxygen flow rates and elevated temperatures increase CO and CO₂ formation, while lower values reduce their production. Changes in n-butane flow have a minor effect on CO₂, but more pronounced effects on CO due to the interplay between partial and complete oxidation at different catalyst sites. Increasing the inlet pressure enhances by-product formation by increasing reactant concentrations, whereas reduced pressure decreases CO and CO₂ formation. The developed model provides a practical tool for understanding and optimizing industrial maleic anhydride production. It offers insights into the effects of key process parameters on by-product formation, supporting improved reactor operation, reduced trial-and-error experimentation, and more efficient industrial process design.

This study aimed to examine the role of recruitment and selection practices in shaping employee organizational commitment in organizations operating in Bosnia and Herzegovina. The empirical research was conducted on a sample of 128 companies from different regions of the country, and the data were collected through a survey questionnaire. The dimensional structure of the research constructs was examined using multivariate statistical procedures, after which the proposed hypotheses were tested through regression analysis. The findings indicate that recruitment and selection practices have a statistically significant and positive impact on overall organizational commitment. In addition, a significant positive relationship was identified between recruitment and selection practices and affective and normative organizational commitment, while the relationship with continuance commitment, although statistically significant, was notably weaker. These results suggest that transparent, fair, and consistently implemented recruitment and selection practices contribute more strongly to employees’ emotional attachment and sense of moral obligation toward the organization than to commitment primarily driven by perceived costs of leaving. The findings are discussed in relation to existing theoretical and empirical research, emphasizing the importance of professional hiring practices for long-term employee commitment. In the specific context of Bosnia and Herzegovina, where organizations face persistent challenges related to employee retention and labor market mobility, the study emphasizes the strategic role of recruitment and selection systems as internal mechanisms for enhancing organizational commitment and fostering stable and sustainable employment relationships.

The purpose of this paper is to reflect on the CONNECT 2025 Symposium in Neum, Bosnia and Herzegovina, from the angle of a junior organizer and a lecturer. We share our journey from the Western Balkans toward academic and research paths in the EU, beginning with CONNECT in 2017 and continuing through studies, research, and PhD work in Germany. Along the way, we highlight why studying abroad matters, what it teaches beyond formal education, and how these experiences can be brought back to strengthen local communities. The contribution also summarizes two accompanying lectures on AI in Science and Gender and Science in the Western Balkans, linking personal experience with broader scientific and societal themes.

This research examines which organizational structure would yield the best results for the operations of a selected agro-food company. To do this, a decision-making model was developed, including six organizational structures and 19 criteria. The model was evaluated by experts using linguistic assessments. In addition, uncertainty was incorporated into this decision-making process through an intuitionistic approach. The results were obtained using the SiWeC (Simple Weight Calculation) and RAWEC (Ranking of Alternatives with Weights of Criterion) methods. The SiWeC method identified employee satisfaction and decision-making speed as the most important criteria, while the RAWEC method found that a digital organizational structure provides the best results. However, to achieve even better results in this company, it is necessary to upgrade the digital organizational structure to a network structure, leveraging the strengths of both. In this way, by forming a hybrid organizational structure, better effects would be obtained than using a single organizational structure.

Reduplication of linguistic units at different levels is a widespread phenomenon both in Arabic in general and in the text of the Qur'an. Therefore, some of its forms, such as lexical repetition, syntactic parallelism, and reduplication at the semantic level, are frequently discussed in the literature as cohesive and text building devices. The main goal of this paper is the analysis of the forms and functions of system intrinsic forms of reduplication in Arabic, i.e. reduplication of grammatical morphemes, derivational patterns, and a specific form of root morpheme reduplication producing a unique semantic effect. The analysis is based on the analytical-descriptive method and the typological-functional approach. Although the reduplication of grammatical morphemes and derivational patterns originates from the core rules of the Arabic language system, the analysis presented in the paper shows that its usage plays a significant role in the text of the Qur'an. In addition to producing of the complex sound patterns of rhyme and rhythm, it also serves as a text-building and cohesive device, and a means of emphasis, reinforcing the content of the message by drawing attention to its form.

Background Delayed diagnosis of brain cancer leads to two-thirds of patients receiving a diagnosis only after presenting to the emergency department with more severe symptoms or neurological deficits. A simple, rapid, liquid biopsy implemented in primary care could enable more efficient triage of patients with non-specific symptoms potentially related to brain cancer, prioritising patients for urgent brain imaging, and accelerating diagnosis. Patients and methods Presented is the international, multi-centre, observational Early and tiMely detection of BRAin CancEr (EMBRACE) study. Patients were prospectively recruited across seven sites in Europe, from the United Kingdom, Belgium, Sweden and Switzerland. The target population consisted of patients with symptoms potentially associated with brain cancer. Blood serum samples were analysed by the Dxcover® Brain Cancer Liquid Biopsy Platform. Test performance was assessed by comparison of the liquid biopsy result to diagnostic imaging. Results Two thousand five hundred and fifty-four patients were enrolled across the seven collection sites; 2324 were deemed eligible and taken forward for test assessment. There were 697 brain tumours in total, of which 395 were malignant, and 1627 non-tumour diagnoses. Overall diagnostic performance for the primary objective was 86% sensitivity for brain cancer detection with 99% negative predictive value (NPV). Sensitivity for all brain tumours combined (malignant and benign) was 77%. Notably, for the most prevalent and most aggressive brain cancer, glioblastoma, 86% of cases were successfully identified. Additionally, 94% of patients with central nervous system lymphoma, and 90% of brain metastases were predicted correctly as having tumours. Conclusions Existing symptom-based referral pathways are ineffective for the detection of brain cancer, and there is an urgent need for new tests to help with clinical decision making. With a NPV of 99%, the Dxcover Liquid Biopsy test could assist in primary care for efficient stratification of patients toward diagnostic imaging.

Gastrointestinal (GI) motility disorders are characterized by abnormalities in the motor functions of the GI tract. The diagnostic evaluation of these disorders frequently involves invasive and time-consuming examinations, for which access may be limited. Volatile organic compounds (VOCs) could serve as non-invasive alternative. Therefore, the aim of this study was to explore the potential of exhaled breath VOCs as biomarkers in patients with GI symptoms and potential GI motility disorders. In this exploratory, prospective study, breath samples were obtained from patients undergoing ambulatory motility tests as part of routine clinical care. VOCs in exhaled breath were assessed using thermal desorption chromatography-mass spectrometry (TD-GC-MS). The resulting data were subsequently pre-processed and analyzed using machine learning approaches. Hundred participants were included in the analysis, of whom 67 were women (67%), with a median age of 56.5 years (IQR: 29.8). The diagnostic work-up comprised 55 gastric emptying tests, 55 high-resolution esophageal manometries, 3 antroduodenal manometries, and 1 colon manometry. These examinations resulted in 48 motility disorder diagnoses, while 51 patients showed no evidence of motility abnormalities. Fifteen VOCs were identified as most discriminative markers for the presence or absence of GI dysmotility, with a sensitivity of 75%, and specificity of 60%. VOCs in exhaled breath show promise to distinguish patients with GI motility disorders from those without, in a population of patients with GI symptoms. Future research is warranted to further refine and validate these results in a larger cohort and to explore the diagnostic performance of VOCs in specific subtypes of motility disorders.

The prevailing paradigm in AI for physical systems: scaling general-purpose foundation models toward universal multimodal reasoning, confronts a barrier at the control interface. Frontier vision–language models achieve only 50–53% accuracy on basic quantitative physics tasks, behaving as approximate guessers that preserve semantic plausibility while violating physical constraints. Safety-critical control demands outcome-space guarantees over executed actions, not parameter-space imitation. Here, we present what is, to our knowledge, the first demonstration of Agentic Physical AI for nuclear reactor control: a domain-specific foundation model in which a compact language model learns control policies through physics-based simulator validation rather than perceptual inference. The framework unifies three capabilities: Agentic AI generates and selects among admissible control strategies; Physical AI evaluates those strategies through closed-loop execution in a reactor simulator; and a domain-specific foundation model acquires reusable control priors through data scaling. Under nominal simulated conditions, scaling yields regime-dependent gains in reliability, including an approximately 500-fold reduction in outcome variance and elimination of terminal-power excursions above 10% on the sampled distribution. Although trained with balanced exposure to four actuation families, the model concentrates 95% of runtime execution on a single-bank strategy without reinforcement learning or reward engineering. The learning framework transfers across reactor simulators without architectural redesign. These results establish a pathway toward reusable, physics-validated foundation-model intelligence for safety-critical control.

In this editorial, we comment on the article published by Qiu et al. Pyogenic liver abscess is a serious clinical condition requiring timely and effective intervention. Ultrasound (US)-guided techniques - whether needle aspiration (NA) or catheter drainage - are key minimally invasive treatments, especially in patients with multiple or deep-seated abscesses where conventional surgery is often impractical. The timing and choice of evacuation method significantly influence clinical outcomes. Although catheter drainage may be necessary for larger or refractory collections, NA represents a less invasive alternative that is often sufficient for smaller abscesses - particularly multiloculated ones - and can avoid multiple catheter placements. This consideration is especially important in the early phase of the disease, when the abscess collection is poorly demarcated from surrounding tissue and more prone to bleeding during or after intervention. Traditional practice delays intervention until liquefaction occurs; however, emerging evidence supports early US-guided evacuation - even in partially liquefied or non-liquefied abscesses - as both safe and effective. Early intervention, particularly via NA when feasible, is associated with faster symptom resolution, shorter hospitalization, and fewer complications. This editorial explores the role of US-guided interventions in pyogenic liver abscess manaement, emphasizing the importance of individualized, timely approaches that optimize disease outcomes while minimizing procedural risk.

The most effective way to ensure drinking water quality and protect public health is the implementation of a preventive, risk-based approach to water supply management. This approach encompasses all stages, from the water source to the consumer, and can be applied across all water supply systems, regardless of their size. The EU Drinking Water Directive (Directive (EU) 2020/2184) emphasizes the importance of this concept. This study assesses and analyzes the risk of groundwater contamination at the water supply sources using a commonly applied risk assessment methodology, which quantifies risk based on the likelihood and potential consequences of hazard occurrence. The methodology for defining risks involved assessing the probability and consequences of certain hazards for the water supply. Based on this, a total of 19 risks were defined, which were prioritized into 4 levels.

Pain in rheumatoid arthritis (RA) often persists despite effective control of inflammation, suggesting distinct mechanisms driving nociception. In both patients and animal models, pain severity does not strongly correlate with the degree of inflammation1,2. Sensory neurons, with cell bodies located in the dorsal root ganglia (DRG), innervate peripheral tissues, including joints, and transmit pain signals to the central nervous system. Crosstalk between sensory neurons and immune cells occurs at all of these sites. While sensory neurons can be directly activated by immune mediators, it remains unclear whether pain-like behaviour in antibody-induced arthritis models arises independently of immune cell activity, or which immune cell populations and mediators are required to activate pronociceptive mechanisms. Through temporal profiling of the CAIA joint–DRG transcriptomic axis, we identified SEMA4D and OSM signalling as candidate molecular mediators of neutrophil–neuron communication and neuronal sprouting. The joint–DRG atlas also revealed persistent changes in the fibroblast-immune cellular composition of the joint, along with molecular changes in DRG neurons. We showed that mechanical and cold hypersensitivity, as well as sprouting of CGRP+ nociceptive fibers in synovial tissue of mice with collagen antibody-induced arthritis (CAIA), require neutrophils but not macrophages. Analysis of publicly available datasets showed that neutrophils from the synovium of RA patients express high levels of SEMA4D and OSM, and corresponding expression of their receptors, PLEXINB1 and OSMR, in human DRG neurons, underscoring the translational relevance of this axis. Both murine and human-derived DRG neurons sprout in response to OSM. Our findings demonstrate that neutrophils produce molecules that act as cues for nociceptor sensitization and structural remodelling. Targeting these molecules could improve the efficacy of RA treatments by reducing pain while simultaneously preventing disease progression.

The cold start period of an internal combustion engine (ICE) is a dominant source of hydrocarbon (HC) and carbon monoxide (CO) emissions. This paper analyzes an innovative ejector system for distributing ionized air directly into the exhaust system to accelerate pollutant oxidation. Analytical results demonstrate that the system achieves an ejection ratio ω = 0.187 at an exhaust temperature of 450 K, while a 180 W heater provides the necessary thermal compensation to maintain the mixture temperature above the 380 K activation threshold. Experimental validation confirmed that ionized air initiates low-temperature oxidation (afterburning), evidenced by a 5.0% vol. increase in CO2 concentration. This synergistic fluid-thermal and chemical strategy reduces the catalyst’s critical inactivity period from 120 s to 85 s, offering highly efficient method for meeting stringent emission regulations.

Background/Objectives: Beyond respiratory problems, COVID-19 can cause a variety of symptoms, such as neurological disorders caused by biological and psychological factors. Brain fog (BF), a post-illness cognitive impairment that many patients report, can be evaluated with reaction time (RT) testing. Response latency is measured by RT, which can be either simple (sRT) or complex (cRT). This study focuses on how COVID-19 affects cognitive function, with particular attention on RT changes, BF prevalence, and implications for daily life. Methods: The study included 599 participants from Bosnia and Herzegovina, Croatia and Serbia. RT was measured using PsyToolkit and participants completed a COVID-19-associated BF questionnaire. Participants who experienced BF after their latest COVID-19 infection rated its severity using a visual analogue scale (VAS). Additional clinical data were obtained from medical records. Results: BF was reported by 40% of participants post-COVID-19. Men reported it less frequently but found it more disruptive. RT progressively declined post-infection, reaching peak impairment at 15 weeks, following recovery, with RT normalizing by six months. Conclusions: COVID-19 is linked to temporary RT impairment, peaking at 15 weeks post-infection and resolving by six months, independent of BF presence. This study emphasizes the need for a biopsychosocial approach to BF management. Easily available RT assessments should be incorporated into routine clinical practice.