This paper presents Aerokinesis, an IoT-based software–hardware system for intuitive gesture-driven control of quadcopter unmanned aerial vehicles (UAVs), developed within the Robot Operating System 2 (ROS2) framework. The proposed system addresses the challenge of providing an accessible human–drone interaction interface for operators in scenarios where traditional remote controllers are impractical or unavailable. The architecture comprises two hierarchical control levels: (1) high-level discrete command control utilizing a fully connected neural network classifier for static gesture recognition, and (2) low-level continuous flight control based on three-dimensional hand keypoint analysis from a depth camera. The gesture classification module achieves an accuracy exceeding 99% using a multi-layer perceptron trained on MediaPipe-extracted hand landmarks. For continuous control, we propose a novel approach that computes Euler angles (roll, pitch, yaw) and throttle from 3D hand pose estimation, enabling intuitive four-degree-of-freedom quadcopter manipulation. A hybrid signal filtering pipeline ensures robust control signal generation while maintaining real-time responsiveness. Comparative user studies demonstrate that gesture-based control reduces task completion time by 52.6% for beginners compared to conventional remote controllers. The results confirm the viability of vision-based gesture interfaces for IoT-enabled UAV applications.
Introduction: the article considers the evolution of language policy in Russia, its historical experience and modern approach to implementation of language policy. The purpose of the article is to give the main characteristics of language policy during the period of the Russian Empire, the USSR, the post-Soviet period and the CIS countries, as well as in modern times, and also to indicate the main risks and problems of language policy of the Russian Federation. Materials and Methods: the article uses research materials such as regulations and official documents. To achieve the goal, research methods such as theoretical analysis of sources and literature are used, as well as a comparative method for studying the language policy of the Russian Federation from the Russian Empire to modern times. Results: the evolution of Russian language policy, its historical experience and modern approach to implementation of language policy are studied. The language policy during the period of the Russian Empire, the USSR, the post-Soviet period and the CIS countries as well as modernity is considered. The main risks and problems of the language policy of the Russian Federation have been identified. Discussion and Conclusion: the results of the study show that the evolution of Russian language policy is a complex process, reflecting historical transformations of the state, its territorial changes and ideological shifts. The modern language policy of the Russian Federation has a clear legal basis. Despite the problems and risks that accompany the language policy of the Russian Federation, its success will depend on the quality of the measures and actions taken, and a combination of legal foundations and actions aimed at preserving the languages of Russia will help to resolve the question of the future of linguistic diversity in Russia.
Introduction: the article is devoted to the analysis of the phenomenon referred to by the authors as the «curse of competence effect» in volunteering. We are talking about a specific form of demotivation and burnout among highly qualified specialists (lawyers, PR and IT specialists, psychologists, etc.) who come to Russian NGOs and volunteer projects. Unlike the classic emotional burnout associated with overload, the key source of exhaustion here is «burnout from inefficiency»: the organization ignores professional recommendations, demonstrates a low managerial standard, and the expertise of a volunteer is unclaimed. The aim of the research is to conceptualize the «curse of competence effect» and its theoretical understanding through the prism of professional identity, cognitive dissonance, social identity, models of emotional burnout and the theory of self–efficacy. Materials and methods: the theoretical basis of the research was the concepts of professional identity, the theory of cognitive dissonance (L. Festinger), the theory of social identity (G. Taschfel), the model of emotional burnout (K. Maslach) and the concept of self-efficacy (A. Bandura). Methodologically, the work is based on the analysis and synthesis of Russian and foreign empirical studies, as well as practical descriptions of volunteer activities in the non-profit sector. The materials used include data from the HSE Center for the Evaluation of Public Initiatives, publications on volunteer burnout, methodological recommendations from the Volunteers of Russia portal, and foreign articles on role uncertainty and professionalization of volunteerism. Research results: it is shown that the professional identity of specialists forms standards of quality, responsibility and consistency, which conflict with the organizational reality of NGOs (lack of resources, chaotic management, blurred roles). Based on the theory of cognitive dissonance, a persistent conflict has been identified between the attitude «I am a competent professional» and the experience «my knowledge is not needed here.» The role of group dynamics and social identity in the formation of the «we are professionals»/ «we are volunteers2 opposition, which increases demotivation, is analyzed. Classical models of burnout are compared with the phenomenon of «burnout from inefficiency», and its specificity is shown: exhaustion does not arise from emotional overload, but from the blocking of professional competencies and the inability to achieve meaningful results. The mechanisms of undermining self-efficacy are considered (A. Bandura) as a central factor of frustration and disruption of the volunteer trajectory. A comparative analysis of Russian and foreign literature is carried out: in Russia, the emphasis is on the emotional and semantic side (guilt, loss of meaning), abroad – on structural role uncertainty and role conflict. Conclusion: the «Competence curse effect» is a natural result of the collision of a strong professional identity with the limitations of real volunteer practice. It leads either to the departure of a professional, or to burnout when trying to stay. For NGOs, this means working with organizational culture, roles, and recognition of expertise; for professionals, it means reflecting their own expectations. Recognizing burnout from inefficiency, along with classic emotional burnout, allows you to get a more complete picture of volunteer work and develop effective strategies for retaining qualified personnel.
This article presents a methodology for developing and implementing a warehouse facility access control and management system (ACS) based on the Siemens industrial controller and the TIA Portal v19 software platform. The functional capabilities of the system are described, including multi-level authorization, time-based access restrictions, emergency security protocols, and automatic lighting control [1]. Configuration tables of inputs/outputs, principles of control logic construction, and features of creating an HMI interface for operational monitoring of the system status are provided [2].
With the increasing penetration of photovoltaic power in power systems, accurate photovoltaic power forecasting is important for dispatch optimization, and renewable energy accommodation. To address the problem that data-driven models may ignore photovoltaic generation mechanisms and produce physically inconsistent forecasting results, this paper proposes a CNN-LSTM-PINN photovoltaic power forecasting method based on data–mechanism fusion. The proposed method introduces physics-informed neural networks into a CNN-LSTM temporal forecasting structure, transforms the physical relationship among photovoltaic power, irradiance, and temperature into differentiable constraints, and constructs physical constraint losses based on partial-derivative direction relationships and power boundary conditions, enabling the model to fit historical data while satisfying photovoltaic generation mechanisms. Experimental results show that the proposed method achieves RMSE, MAE, and R2 values of 1.0583, 0.5727, and 0.8912 on the photovoltaic plant dataset, respectively, outperforming PINN CNN-LSTM, LSTM, and traditional machine learning models. Under a 0.30 noise pertur-bation level, its RMSE increases by only 2.86%, demonstrating good stability and anti-disturbance capability.
