PurposeThis study aims to assess the extent to which the constructs of theory of planned behavior drive the social entrepreneurial intention (SEI) of the youth and provide a nuanced understanding of the role of prosocial behavior in shaping this intention.Design/methodology/approachA questionnaire was used to collect data with a sample size of 690 students, regarded as youth, across higher education institutions in Oman. Data analysis was performed using SPSS 25.0 and partial least squares structural equation modeling.FindingsThe results of the preliminary analysis confirmed that subjective norms toward social entrepreneurship (SE) and perceived behavioral control over SE were generally regarded as factors strongly associated with SEI. However, attitude toward SE did not affect the intention of the youth differently in this measure. Moreover, a positive correlation was found between prosocial behavior and SEI. Other results and implications were discussed.Originality/valueThe proposed SEI model sheds light on the possible influence of the prosocial behavior construct on the youth’s intention to be engaged in SE. The findings may help governments, policymakers and decision makers to promote positive youth development in three domains: education, labor market and community.

Children and youth have predominantly shifted social activities into the digital sphere, leading to the consistent prevalence of digital technologies in the classroom. Besides numerous benefits, it also brought challenges, such as (cyber)bullying. Many teachers may approach the issue of cyberbullying with reservations, either because they do not view it as a serious problem or believe that they should not play a role in its resolution. Therefore, it is crucial to provide teachers and librarians with professional development on the challenges and risks of cyberbullying among pupils. Using quantitative method, this paper examines the competencies and experiences of teachers and librarians in Bosnia and Herzegovina in addressing the issue of cyberbullying among pupils. The importance of ongoing professional development and the availability of education in recognising risky behaviours of pupils online is particularly emphasised. The data indicates that, despite their engagement in lifelong learning, teachers and librarians require systematic and continuous training to understand and respond promptly to emerging digital risks involving children.

Background: The process of prenatal hematopoiesis occurs in various anatomical locations, including the placenta. The placenta is not merely a temporary hematopoietic reservoir, but it is one of the key sites for the synthesis of hematopoietic stem cells (HSCs). This study aimed to investigate the presence, distribution, and immunoprofiles of HSCs in the human placenta during different gestational periods. Materials and Methods: Placental samples of different gestational ages (first, second, and third trimesters) were analyzed using classical hematoxylin and eosin staining and immunohistochemical staining for CD34, CD117, and CD41 markers, with HSC quantification through numerical areal density (NA). Results: Highly immunoreactive CD34 HSCs were present in placentas throughout gestation, while highly immunoreactive CD117 and CD41 HSCs were observed during the first two trimesters. In the first trimester, HSCs were found within the lumen of blood vessels and as individual cells in the mesenchyme of chorionic villi. With advancing gestation, the number of HSCs in the mesenchyme of chorionic villi increased. Conclusions: Immunoreactive CD34, CD117, and CD41 cells are present in significant proportions in various parts of the placenta throughout gestation, indicating that the placenta provides a substantial proportion of HSCs for hematopoiesis.

Above-threshold detachment of electrons from negative ions by a strong low-frequency elliptically polarized laser field is considered using the strong-field approximation. The detachment probability amplitude is expressed via integral over times of highly oscillatory functions. Particular attention is devoted to application of the asymptotic methods to solve these integrals. For the direct detachment only the integral over the detachment time appears, while for the high-order above-threshold detachment the double integral over the detachment and rescattering times should be solved. Depending on the ellipticity of the laser field, a critical photoelectron energy exists for which the standard saddle-point method fails. The problem can be solved by properly deforming the integration contour in the complex time plane and, for energies higher than this critical energy, taking into account only one of the two saddle-point solutions. However, this procedure still leaves a spike in the photoelectron spectrum near this critical energy. This problem is cured applying the uniform approximation. A formula for the transition amplitude in the uniform approximation is derived, and it is shown how this formula should be modified for the energies higher than the critical one. For high-order above-threshold detachment many more saddle-point solutions contribute. They are classified into pairs. For the saddle-point method each pair produces a spike in the spectrum which spoils the total spectrum. When the contribution of each pair is treated using the uniform approximation with a careful choice of the phase factors after the anti-Stokes transition the agreement with the exact numerical results becomes excellent. Published by the American Physical Society 2025

This study proposes a sustainable multi-criteria optimization framework for the energy retrofit of collective residential buildings in Algeria, particularly those constructed between the 1970s and 1980s. Through on-site surveys, energy consumption analysis, and seasonal temperature measurements, the high energy demand of these buildings was confirmed. Using EnergyPlus simulations based on Meteoblue weather data, 16 retrofit strategies were assessed—incorporating various insulating materials applied internally or externally (via rendering or cladding). The ELECTRE III decision-making tool was employed, supported by the Simos Revised Framework (SRF) for weighting environmental, economic, and social criteria. Results demonstrate that all strategies significantly reduce energy demand—by up to 72.5%, with reductions reaching 94.4% in winter and 43.5% in summer, depending on insulation type and placement. Improvements in indoor thermal comfort were also observed, with exterior insulation beneath cladding offering the best performance during winter, while exterior rendering also proved effective in the summer. The ELECTRE III analysis identified rock wool and polyurethane with fiber cement cladding as optimal insulation solutions. The proposed approach supports national energy policies and aligns with the Sustainable Development Goals (SDGs), offering a replicable model for large-scale building retrofits in similar climatic and architectural contexts.

This paper proposes a novel approach to the virtual 3D modeling of articulated mechanisms. It follows the widespread use of XML (eXtensible Markup Language) for various applications and defines a version of XML that is specially designed for the description of 3D geometric models of articulated bodies. In addition, it shows how the 3D geometric model of a mechanism can be gradually developed through the use of suitably defined elements and stored in a corresponding XML file. The developed XML model is processed, and using a powerful VTK (Visualization Toolkit) library, the corresponding virtual model is built and shown on the computer screen. To drive the virtual model, the dynamic model of the mechanism is developed using Bond Graph modeling techniques. Virtual 3D geometric and dynamic models are created using the corresponding software packages: BonSim3D 2023 Visual and BondSim 2023. The models are interconnected by a two-way named pipe. During the simulation of the dynamic model, the parameters necessary to drive the virtual model (e.g., the joint displacements) are collected and sent to the virtual model over the pipe. When the virtual model receives a package, the computer screen is updated by showing the new state of the mechanism. The approach is demonstrated using the example of a holonomic omnidirectional mobile robot.

With the discovery of the main bioactive compounds, royal jelly (RJ) takes a significant role in the food and pharmaceu-tical industry. One of the most important ingredients of RJ is 10-hydroxy-2-decenoic acid (10-HDA). In this paper, ten samples of RJ were analyzed, which were collected from the area of northwestern Bosnia and Herzegovina. In addition to 10-HDA, glucose, fructose, sucrose, and maltose content, physicochemical parameters were analyzed: pH value, total acidity, water content, protein content, and antioxidant activity of RJ. The obtained results show that samples of RJ meet international standards with regard to the content of 10-HDA. Considering the established quality and very high antioxi-dant activity of the analyzed samples (analyzed with DPPH and FRAP methods) they have significant potential in devel-opment of functional products with pronounced nutritional and biological capacity. This work is first attempt in estab-lishment of RJ quality criteria in this area.

This study aims to estimate the number of AMY2B gene copies and measure serum amylase activity in several Balkan dog breeds. Additionally, it explores the relationship between these genetic and biochemical parameters. Blood samples from 85 dogs representing eight breeds were collected, DNA was extracted, and AMY2B copy numbers were determined using droplet digital PCR. AMY2B gene copies ranged from 7.7 to 18.4, with a mean of 12.4 ± 2.2. Significant breed-related differences were observed (p = 0.025), with Istrian Wire-Haired Hounds showing the highest mean copy number (13.9 ± 1.5) and Posavatz Hounds the lowest (10.8 ± 1.5). Serum amylase activity ranged from 3.3 to 17.8 µkat/L, with a mean of 8.7 ± 2.6, and showed significant interbreed differences (p = 0.004), with Barak breed displaying the highest activity. Serum glucose levels varied widely, but no significant interbreed differences were detected (p = 0.340). No significant correlation was found between AMY2B copy numbers and serum amylase activity or glucose levels. The study concludes that Balkan dogs have AMY2B copy numbers similar to other European breeds, likely reflecting historical agricultural practices in the region, thereby facilitating better starch digestion. While significant variations exist among breeds, the lack of correlation between gene copy number and amylase activity suggests that other factors influence enzyme levels.

Immunotherapy has revolutionized survival outcomes for many patients diagnosed with cancer. However, biomarkers that can reliably distinguish treatment responders from nonresponders, predict potential life-threatening and life-changing drug-induced toxicities, or rationalize treatment choices are still lacking. In response to this unmet clinical need, we introduce Multiomic ANalysis of Immunotherapy Features Evidencing Success and Toxicity, a tumor type-agnostic platform to provide deep profiling of patients receiving immunotherapy that will enable integrative identification of biomarkers and discovery of novel targets using artificial intelligence and machine learning.

This work developed and evaluated a rapid, cost‐effective, and mobile analytical method for the quantification of stabilizers in nitrocellulose‐based propellants, using thin‐layer chromatography and image densitometry. The method demonstrated effectiveness in the separation and quantification of diphenylamine and N‐nitrosodiphenylamine, showing linearity, accuracy, and precision for concentrations above 0.3 wt%. To address limitations at low concentrations, a screening test approach with a cutoff value of 0.3 wt% was proposed. Innovations included a custom‐built dark chamber to improve photographic records, and a custom script for automated image analysis, using open‐source software. The developed analytical procedure, along with the supporting tools created, offers a promising solution for rapid and efficient field monitoring of nitrocellulose‐based propellant stability, with the potential to complement existing laboratory methods in ammunition surveillance.

This study introduces an AI-based framework for stroke diagnosis that merges clinical data and curated imaging data. The system utilizes traditional machine learning and advanced deep learning techniques to tackle dataset imbalances and variability in stroke presentations. Our approach involves rigorous data preprocessing, feature engineering, and ensemble techniques to optimize the predictive performance. Comprehensive evaluations demonstrate that gradient-boosted models outperform in accuracy, while CNNs enhance stroke detection rates. Calibration and threshold optimization are utilized to align predictions with clinical requirements, ensuring diagnostic reliability. This multi-modal framework highlights the capacity of AI to accelerate stroke diagnosis and aid clinical decision making, ultimately enhancing patient outcomes in critical care.