A. Grekhov and V. Kondratiuk, “Low-Complexity AI for Adaptive Packet Transmission in Interference-Limited UAV-to-Cluster Cellular Scenarios: Simulation Results,” Trans. Appl. Sci. Eng. Technol., vol. 2, no. 1, pp. 1-9, 2026.

This paper presents a simulation-based study of artificial intelligence (AI)-assisted communication channel adaptation in unmanned aerial vehicle (UAV)-enabled cellular networks. The considered system model includes a communication channel “Ground Base Station – Aerial Repeater – UAV Base Station – Cluster of Cellular Network Users”. The primary objective of the study is to investigate the impact of adaptive channel parameter control on communication performance under dynamically changing interference conditions.

A lightweight supervised machine learning approach based on linear regression is employed to implement cognitive channel adaptation. The AI model operates on packet-level performance indicators and enables real-time adjustment of transaction size (TS) in response to variations in bit error rate (BER) and effective data rate. A custom simulation environment is developed to generate training and testing datasets and to evaluate system behavior under both static and adaptive channel configurations.

The performance of the proposed AI-assisted adaptation scheme is assessed using latency, channel average utilization (AU), and packet transmission characteristics as key metrics. Simulation results demonstrate that adaptive control of TS improves latency stability and channel utilization compared to a static configuration, particularly in high-interference scenarios. The findings confirm that even computationally efficient AI models can provide measurable performance benefits for UAV-assisted cellular communication systems and are suitable for implementation on resource-constrained UAV platforms.

P.E.F. Pinto Coelho, “Zero Waste Through Processing Municipal Solid Waste (MSW) to Refuse Derived Fuel (RDF) and Energy Production While Minimizing Negative Environmental Impact,” Trans. Appl. Sci. Eng. Technol., vol. 2, no. 1, pp. 1-6, 2026.

This study addresses the critical environmental and logistical challenges of modern municipal solid waste (MSW) management by proposing an integrated system that transitions from traditional "in natura" incineration and landfilling toward a zero waste model. Traditional waste disposal methods are often plagued by operational fluctuations, toxic emissions (such as dioxins and heavy metals), and significant contributions to greenhouse gas emissions. This research presents a technological framework that uses mechanical, thermal, and manual processes to convert MSW into refuse derived fuel (RDF), ensuring a more homogeneous and controllable combustion process.

A. Rodríguez-Jiménez, Y.C. Abreu Álvarez and Z.H. Castro, “Self-Regulated Learning Through Cooperative Learning Methodology and Formative Assessment,” Trans. Appl. Sci. Eng. Technol., vol. 2, no. 1, pp. 1-8, 2026.

The development of self-regulated learning in university students is a goal to achieve in facing the educational demands of the 21st century at «Julio González» Artemisa University. This paper intends to assess the influence of evaluation and the use of the cooperative learning methodology in the self-regulation of the participants' learning in a post grade course on evaluation in the teaching-learning process (TLP). To fulfill the objective, an observation guide was applied to five sessions through which the cooperative learning methodology was implemented. In addition, a self-assessment survey for the participants was put into practice to explore their perception of the influence of the course on their self-regulated learning. The results indicated that the integration of formative assessment on the cooperative learning methodology during the post grade course contributed positively to the development of the participants' self-regulated learning.

V. Kharchenko, A. Grekhov and V. Kondratiuk, “Cognitive Adaptation of Modulation for Improving Noise Resistance of a Tropospheric UAV Channel,” Trans. Appl. Sci. Eng. Technol., vol. 2, no. 1, pp. 1-8, 2026.

In modern operational conditions of unmanned aerial vehicles (UAVs), the stability and reliability of communication channels are becoming key factors in the successful completion of missions. Operation in a dynamically changing radio environment, including the influence of natural interference and electronic warfare (EW), is especially relevant. Traditional communication systems with a fixed modulation scheme cannot effectively respond to changes in channel parameters, such as signal level, signal-to-noise ratio (SNR), and the presence of interference. This leads either to excessive errors (with excessive modulation) or to an unjustified loss of throughput (with increased reliability under good conditions). One of the key areas for improving noise immunity is the cognitive adaptation of modulation. To study the influence of cognitive adaptation on noise immunity in the UAV communication channel, an original model, “Base Station-Tropospheric Drone-UAV,” was built using the MATLAB Simulink software. The dependences of the SNR ratio on the Free Space Path Loss (FSPL) for different types of signal modulation and noise temperatures were obtained. The calculated interrelated sequences of FSPL, SNR, and modulation were used to adapt the modulation in accordance with changing transmission conditions. The model was trained on the obtained data and then used for modulation adaptation. Python code is given that uses the Random Forest Classifier model to predict the modulation type.