The introduction of autonomous mobile robotic systems into ground handling operations at airports has been limited by aviation safety requirements and the high costs associated with a robot colliding with an aircraft. To ensure safety, traditional robotic navigation methods use static buffer zones, which limit the functionality of robotic systems working near the aircraft fuselage. A probabilistic risk assessment model was developed in this study to simulate close-in operation of heterogeneous mobile robotic systems around an aircraft. The proposed model employs a hybrid framework that integrates an extended Kalman filter, Monte Carlo simulations, and a Bayesian network to consider the kinematic uncertainty of a robot, random environmental conditions, and sensor data for real-time evaluation of collision probabilities. The modeling of near-aircraft inspection scenarios conducted in MATLAB demonstrated the feasibility of the proposed approach: the system successfully completed 49 out of 50 simulated missions while testing landing gear inspection scenarios. In addition, the modeling reduced the minimum distance to the inspection object to 0.48 m, compared with a baseline safe distance of 2 m. These results are interpreted as a simulation-based verification of the feasibility of the proposed approach rather than as an operational validation. Experiments, including hardware modeling, data from real sensors, and controlled tests on the airport apron, are required before implementing the approach in real-world conditions.
IntroductionThe selection of functionalized implant coatings is a complex multi-criteria decision-making problem because biological, engineering, technological, and implementation-readiness indicators are heterogeneous and are frequently evaluated manually. This study proposes an automated and explainable computational decision-support framework for coating assessment and ranking.MethodsThe framework integrates data preprocessing, indicator normalization, weighted scoring, implementation-readiness assessment, sensitivity analysis, explainable classification, and visualization. Biological effectiveness, engineering and operational reliability, and manufacturability and scalability were used as the three evaluation domains. Published experimental data from 2015 to 2025 on titanium oxide nanostructures, hydrogel coatings, hybrid nanocomposites, and bioactive surface modifications were used for validation.ResultsThe framework generated reproducible domain scores, integral performance scores, implementation classes, and explainable rankings. TiO₂-Sr, Grad-Ti, and TiO₂-NT achieved the highest scores because of their balanced biological performance, engineering reliability, sterilization compatibility, and technological scalability. The ranking remained stable under variations in weighting factors.DiscussionThe proposed framework transforms heterogeneous biomedical data into a transparent and reproducible decision-support workflow. It can serve as the computational core of future biomedical material-selection platforms and support preliminary coating screening, technology-readiness evaluation, and data-driven engineering decisions.
Technological breakthroughs in the rapidly developing aviation industry have exacerbated the problems with associated assessing the competencies and systems-thinking skills of personnel. Traditional assessment methods based on static models and measuring previously acquired knowledge are losing relevance because they fail to capture critical indices such as flexible thinking, adaptability, and a willingness to continuously learn. Herein, a comprehensive method for assessing the competencies and systems-thinking skills of civil aviation personnel was developed. Item response theory was applied to survey data from the personnel of an airport in Kazakhstan to develop latent competency indices. The relationships between indices were considered through a dynamic Bayesian network and their dynamics were predicted up to 2030 across three development scenarios. The Technique for Order of Preference by Similarity to Ideal Solution method integrated the individual indices into a single metric the human capital readiness index that quantitatively assessed competencies and systems-thinking skills in the context of rapid technological changes. The results demonstrated the ability of the proposed method to objectively assess the potential of civil aviation personnel in a dynamic and technology-intensive environment, confirming the need to rethink traditional approaches to personnel assessment. The proposed method is expected to facilitate the development of targeted training and retraining programs as well as the establishment of qualification standards for civil aviation personnel.
Abstract This paper presents an approach for creating a digital seismic twin of a building using a dense network of low cost sensors and intelligent analysis of vibration time series. Vibration data recorded at different levels of the structure are processed using statistical features describing the intensity and variability of motion. Anomalous behavior is detected using a machine learning method trained on normal operating conditions. The results show that normal structural states form a compact group in feature space, while abnormal and transitional states are located outside this group. The proposed approach enables early detection of weak dynamic changes and demonstrates potential for real time monitoring and improved safety of urban structures. In addition, the system architecture supports continuous data acquisition and scalable processing, allowing integration into existing monitoring infrastructures. The proposed framework can be applied to various types of buildings and urban environments, providing a cost-effective solution for structural health assessment. Future work includes incorporating advanced deep learning models and validating the approach on larger datasets under diverse environmental conditions.
Background: The study investigates the level of expression, interrelations, and statistically significant differences in social intelligence within the context of students' educational and professional activities. Material/ Methods: The sample consisted of second-and third-year university students representing various fields of study in Kazakhstan Universities. A set of psychodiagnostic instruments adapted and validated for the Kazakhstani population was applied. Results: Average indicators of learning styles and thinking styles were obtained. In terms of social intelligence, students are encouraged to strengthen their competence in verbal expression and to develop logical reasoning that supports effective behavioural responses. These competencies were shown to be associated with creativity and the prognostic aspects of thinking. Significant male advantages were identified in four areas: (1) practical learning style, (2) ability to solve tasks based on behavioural story completion, (3) ability to solve tasks requiring logical completion of behavioural sequences, and (4) analytic thinking style. Significant female advantages were observed in three areas: (1) activist learning style, (1) competence in verbal expression, and (3) realist thinking style. Among learning styles, the activist style demonstrated the highest dependence on external factors, whereas the reflector and pragmatist styles appeared the most autonomous. Conclusions: The findings support the view that social intelligence, within educational and professional activities, reflects students' ability to understand others, interact effectively in the learning environment, and solve cognitive tasks relevant to professional development. It is recommended that university educational planners consider these results when designing programmes aimed at enhancing students' social and cognitive competencies.
