Lantung bark (Artocarpus elasticus) biocomposite is a natural fiber-based material exhibiting potential as an eco-friendly alternative due to its competitive mechanical properties. This research evaluates the mechanical properties and morphology of polyester resin-based composites reinforced with lantung bark, specifically utilizing silane modification to enhance matrix compatibility. The biocomposites were fabricated using the hand lay-up method with varying silane concentrations of 2%, 4%, and 6%, followed by characterization through tensile tests, bending tests, and 3D imaging analysis. The results indicated that the sample with 2% silane concentration (N6S2) achieved the highest tensile strength (31.20 MPa) and bending strength (74.08 MPa). Meanwhile, the highest Modulus of Elasticity (MOE) for both tensile (2.54 GPa) and bending (3915.59 GPa) was observed in the sample with 4% silane concentration. However, increasing the silane concentration to 6% resulted in a decrease in mechanical strength, attributed to plasticization or adhesion interference as confirmed by morphological analysis. Overall, the research demonstrates that a 2% silane concentration yields the optimal performance, making this biocomposite suitable for lightweight structural applications, such as automotive interior parts. The object of research: the biocomposites made from Artocarpus elasticus (lantung bark) fibers reinforced with silane-treated polyester resin, targeted for automotive interior applications. Investigated problem: the core problem is the lack of data on how silane matrix treatment influences mechanical properties and morphology of Artocarpus elasticus (lantung bark) biocomposites, limiting their optimization for automotive interiors. The main scientific results: silane treatment at 2% concentration optimizes Artocarpus elasticus (lantung bark) biocomposites, achieving peak mechanical performance exceeding SNI standards (tensile >4 MPa, bending >35 MPa). The area of practical use of the research results: lantung bark (Artocarpus elasticus) biocomposites with 2% silane treatment serve as lightweight, sustainable alternatives for automotive interiors, meeting/exceeding SNI standards (tensile >4 MPa, bending >35 MPa). Innovative technological product: the optimized silane-reinforced Artocarpus elasticus (lantung bark) biocomposite using 2% silane in polyester resin (post-NaOH fiber treatment). Scope of the innovative technological product: encompasses non-structural automotive interiors using optimized Artocarpus elasticus (lantung bark) biocomposites with 2% silane-treated polyester resin

The object of research: The object of the conducted research is the processing of EEG signals in motor imagery (MI)-based brain-computer interface technologies and the application of machine learning (ML) algorithms for controlling prosthetic devices using these signals. Investigated problem: The main problem addressed in the research is ensuring the stable and reliable operation of real-time BCI-based prosthetic systems due to the noisy and non-stationary nature of non-invasive EEG signals. In particular, the research investigates the accurate discrimination of neural activity generated during motor imagery and the ability of algorithms to operate without latency in real-time conditions. The main scientific results: As a research result, a comparative analysis of the effectiveness of three different algorithms – support vector machine (SVM), linear discriminant analysis (LDA), and random forest – was conducted in the classification of EEG signals. The results revealed that linear models achieve higher accuracy and are more stable and reliable for motor imagery-based BCI systems. The area of practical use of the research results: The research results can be applied in medical and technological institutions and research centers engaged in the development of intelligent prosthetic devices controlled by brain signals, rehabilitation systems, exoskeletons, and other assistive technologies. Innovative technological product: A BCI technology utilizing SVM and LDA algorithms is proposed for optimizing real-time BCI-based intelligent prosthetic systems. Scope of the innovative technological product: The proposed innovative technology can mainly be applied in the clinical use of neuroprosthetics, in the restoration of motor functions in patients suffering from stroke and neurological disorders, as well as in real-time human–machine interaction systems

Subject of research: Subject of research: The object of research is the security infrastructure of intelligence-enabled environments such as IoT systems and cyber-physical systems that involve high levels of heterogeneity, mobility, as well as constantly changing boundaries of operations. Investigated problem: The identified problem description relates to the inefficacy of classical perimeter security concepts in a scaled-up intelligence environment where the boundaries of a network are no more precise, making trust a concept difficult to define by location. The main scientific results: The study proposes an analysis framework with which the principles of Zero Trust can be applied to smart systems following the guidelines of NIST SP 800-207. The research highlights the vulnerabilities associated with multi-level intelligent environments and has outlined the essential elements of effective Zero Trust Architecture to be adapted to resource-constrained scenarios. It has concluded that to adequately safeguard IoT and cyber-physical systems, the need arises to integrate the aspects of continuous verification, context-responsive access control, and adaptive trust assessments. The research has shown that the integration of the aspects of artificial intelligence and machine learning with predictive threat analysis, along with the usage of blockchain identity management, highly enhances the resilience level of the system. The area of practical use of the research results: The research results can be applied in the implementation of secure IoT ecosystems, cyber-physical systems, cloud infrastructure, and enterprise networks that require a high level of adaptability to changing threats. Innovative technological product: The innovative technological product that has been described in the research paper is a Zero Trust Architecture framework that has been designed for smart environments which are diverse and resource-scarce. Scope of the innovative technological product: The proposed framework is universal in application, as it spans smart cities, industrial IoT, healthcare IoT, smart areas, smart buildings, smart transportation systems, smart grids, smart energy, smart homes, smart education, smart industries, smart governance, smart IoT, smart security, smart surveillance, smart sensors, smart

The object of research: The object of this research is the investigation of the power quality effects caused by DC fast-charging systems used for electric vehicles (EVs). The study focuses on the harmonic distortions introduced into the electrical grid and evaluates their impact on system reliability and battery performance using MATLAB/Simulink-based simulations. Investigated problem: The rapid expansion of EV charging infrastructures has led to increased harmonic pollution in distribution networks, resulting in waveform deformation, transformer losses, and decreased efficiency. The problem lies in the insufficient filtering of DC charging stations, which act as nonlinear loads and deteriorate the grid’s sinusoidal waveform. The main scientific results: Simulation results demonstrate that the implementation of an LCL filter in DC charging stations reduces total harmonic distortion (THD) from 184.69% to 58% for battery SOC and from 31–33% to 13–14% for current and voltage. FFT analysis confirmed a 60–80% suppression of low-order harmonics (2nd, 3rd, 5th). Thus, LCL filters significantly enhance power quality and charging stability. The area of practical use of the research results: The findings can be applied in the design and optimization of DC fast-charging stations to ensure compliance with IEEE 519 standards. Grid operators, EV infrastructure developers, and energy planners can utilize these results to minimize harmonic interference and improve the integration of electric mobility into power systems. Innovative technological product: An LCL-based harmonic mitigation model for DC fast-charging stations was developed and validated through simulation. This configuration offers a cost-effective passive filtering solution that can be easily integrated into existing charging systems without requiring complex active control mechanisms. Scope of the innovative technological product: The proposed model is suitable for urban fast-charging networks, industrial charging hubs, and renewable energy-integrated microgrids. Its scalability and adaptability make it ideal for next-generation smart grid applications where maintaining high energy quality is essential

Listening is one of the most essential skills in everyday communication. The ability to understand spoken language in a foreign language and respond appropriately, like all other language skills, requires systematic instruction and practice. The object of research is the study of the ways of developing listening skills in English for specific purposes through the use of authentic English podcasts in tertiary education. Investigated problem concerns the difficulties students experience when listening independently to authentic podcast materials and the discrepancy between learners’ self-assessment of listening performance and their actual listening competence. The main scientific results demonstrate that learners’ self-assessment of listening skills largely corresponds to their classroom listening performance. It is shown that repeated listening, the use of transcripts, dictionary, and contextual inference significantly foster listening comprehension. In particular, integrating individual podcast-based listening with classroom discussion motivate students as a result of which their autonomy and awareness of effective listening strategies increase. The area of practical use of the research results includes higher education institutions offering English for specific purposes, particularly in non-linguistic faculties. Innovative technological product is a podcast-based listening methodology combining online listening, self-assessment and classroom activities in order to develop listening skills. Scope of the innovative technological product includes tertiary-level English for specific purposes instruction, mixed learning environments and self-study contexts

Research object: Fatigue life determination for cast iron, aluminum, hardened brass, unheated treated brass, annealed brass, hardened copper, annealed copper, PVC, ABS high impact (grade A) and ABS super high impact (grade B) each mixed with ABS scrap with a composition of 10%, 20%, and 30%, Acrylic, and E-glass epoxy composite. Problems studied: Fatigue life prediction for cast iron, aluminum, hardened brass, unheated treated brass, annealed brass, hardened copper, annealed copper, PVC, ABS high impact (grade A) and ABS super high impact (grade B) each mixed with ABS scrap with a composition of 10%, 20%, and 30%, acrylic, and E-glass epoxy composite. Main scientific results: Bending stress versus fatigue life curves of cast iron, aluminum, hardened brass, unheated treated brass, annealed brass, hardened copper, annealed copper, PVC, ABS high impact (grade A) and ABS super high impact (grade B) each mixed with their ABS scraps with a composition of 10%, 20%, 30%, acrylic, and composite E-glass epoxy. Practical application areas of research results: As design data suitable for construction using metal and polymer materials. Innovative technology products: relatively accurate data predictions compared to data obtained from textbooks, where the values are generally still within the minimum to maximum range, which raises doubts about their accuracy for designers. Scope of innovative technology products: The fatigue testing machine used for fatigue life prediction is a homemade creation based on R.R. Moore's working principle using a serial load suspended from the shaft being tested in rotation.

The object of research: This research explores how digital educational resources and online applications can be used in primary general education to support teaching and enhance young students’ learning experiences. Investigated problem: The research focuses on the challenge of integrating technology into early primary education in a way that is developmentally appropriate, engaging, and promotes meaningful interaction between teachers, students and parents. The main scientific results: The findings show that blended learning is the most effective approach for young learners, combining traditional classroom instruction with interactive digital tools. Digital resources not only boost students’ motivation and participation but also simplify assessment and help tailor learning to individual needs. Digital tools are categorized based on their educational role: learning platforms, cloud collaboration tools, test-making applications, virtual whiteboards, gamified learning environments, and programming platforms, each offering unique ways to enhance instruction. These resources make lesson preparation more efficient, automate routine evaluation, and give teachers insight into student progress while allowing learners to revisit materials and consolidate knowledge independently. Integrating these tools enriches teaching by fostering active learning, supporting skill development, and helping students acquire both theoretical knowledge and practical abilities essential for lifelong learning. The area of practical use of the research results: The results can guide curriculum design, instructional planning, and teacher professional development programs for effectively incorporating ICT in primary education. Innovative technological product: Key technological solutions highlighted include MOODLE, cloud-based collaboration platforms, virtual whiteboards, gamified learning tools, and the localized Logo programming environment (BurLogo), all supporting blended learning practices. Scope of the innovative technological product: These technologies are versatile and can be applied to classroom teaching, remote learning, individualized study plans, and collaborative projects, enhancing both student engagement and teaching efficiency

The object of the research: the individual educational trajectory of a teaching professional. Investigated problem: building an individual educational trajectory for teaching staff in the context of continuing education and its reform in Ukraine. The main scientific results: – continuing education is considered as a continuous process of learning and improving the professional competencies of specialists. The goals of continuing education are determined, it is proven that self-education is the leading form of continuing education, which determines the search for, and the implementation of, individual ways of achieving it; – data on the advantages of organizing training and development of a pedagogical worker are summarized and systematized based on the implementation of an individual educational trajectory for both the educational institution and the pedagogical worker himself or herself; – the forms of implementation of an individual educational trajectory in the system of continuous pedagogical education are determined. Possible ways of implementing an individual educational trajectory in the system of continuous pedagogical education are demonstrated. Internal and external factors influencing the choice or the structure of an educational trajectory, the choice of forms and methods of education, the development of educational models, and the tools for measuring results, are presented; – data on determining the directions of an individual trajectory of education and self-education and the expected results of their implementation are summarized. It is emphasized that the process of choosing and building an individual trajectory of education and self-education is continuous. Factors for implementing continuous education and the conditions for their successful implementation are determined. The area of practical use of the research results: education, particularly training and improving the activities of teaching staff. Innovative technological product: summarizing ways to implement continuing education, defining the tasks of scientific and methodological support for the general secondary education system, identifying signs of a general crisis in education and its consequences, and identifying the main directions for the development of continuing education based on an analysis of literary data from Ukrainian researchers. Scope of the innovative technological product: postgraduate education, particularly in matters of improving professional knowledge, skills, and abilities based on already acquired higher or vocational education, deepening knowledge in one's field, or gaining practical experience.

The object of research. The development of compatible security frameworks that align with civil and building construction information systems and BIM was undertaken. The paper examined existing literature on traditional construction management concepts and the systems view of building construction projects. It explored information systems security frameworks for the deployment of cybersecurity principles; examined the risk management in the world of construction management and BIM. This was with the view to developing security protocols that protect BIM data from DLP threats. Investigated problem. BIM and smart construction rely heavily on continuous data exchange leaving them exposed to cyberattacks. Previous researches in the field confirmed there was no existing cybersecurity standards that are directly applicable to construction project management. The dearth of information on the applicability of the existing ICS framework to construction management warrants that it is develop a new compatible framework solution that accommodate the dynamic capabilities of BIM and the scope, complexity among other of smart construction systems. The main scientific results. The results showed that the NIST-CSF 2.0 (NIST Cybersecurity Framework), fine-tuned with some adaptabilities in relation to each specific project, is the most compatible and recommended security framework for BIM and CIIS, offering flexibility, scalability, and alignment with both information security and management needs. It also supported the fact that smart construction management is fast becoming a cyber-physical endeavor due to the extended reliance on digital platforms. The area of practical use of the research results. Hence, the increasing call for government agencies such as the Federal Housing Authority (FHA) of Nigeria to develop construction-specific cybersecurity guidelines. It is also recommended that these agencies should integrate governance, risk management and compliance (GRC) into building approval processes. Innovative technological product. The adoption of the new framework will empower construction firms to move from reactive to proactive cybersecurity frameworks while deploying ISO 27001 and NIST standards. Firms will hence be positioned to integrate cybersecurity metrics into project performance KPIs. Scope of the innovative technological product. Cybersecurity must be recognized as a foundational pillar of smart construction and sustainable development in the built environment. Strategic investment, supportive policy frameworks, and capacity building are essential to achieving resilient and secure construction information systems. This is with a view to resolving all conundrum surrounding BIM and building construction for enhanced safety and security of project deliverables.

The object of research: The object of research is the non-performing loan (NPL) risk prediction system in the banking sector. Investigated problem: The challenge of handling severe class imbalance in large-scale banking datasets, where NPL cases are significantly underrepresented, leading to biased and inaccurate predictive models. The main scientific results: A balanced dataset was obtained by applying random undersampling (RUS), reducing the original dataset of 2.58 million records to approximately 62,400 records. An extreme gradient boosting (XGBoost) algorithm was developed for NPL classification, with hyperparameter tuning performed using particle swarm optimization (PSO). The optimized model achieved an F1-score of 80%, reflecting a good balance between precision and recall. SHAP (SHapley Additive exPlanations) analysis revealed that principal disbursement, outstanding balance, and pricing were the most influential features in predicting credit default. The study also demonstrated that improved model performance and interpretability can be achieved through this integrated approach. The area of practical use of the research results: Banking and financial institutions engaged in credit risk assessment and management for loan portfolios. The research outcomes can be directly applied in the development of automated credit screening tools to identify high-risk borrowers early in the credit lifecycle. This supports financial institutions in minimizing default rates, optimizing portfolio quality, and complying with regulatory requirements such as IFRS 9 and Basel III. Innovative technological product: A credit risk prediction framework integrating XGBoost, PSO, and SHAP analysis that enables accurate and interpretable classification of Non-Performing Loans. This framework uniquely combines a high-performance ensemble machine learning model (XGBoost), a global optimization algorithm (PSO) for fine-tuning model parameters, and explainable AI (SHAP) to provide transparency. This fusion empowers credit analysts to trust model predictions and act upon them confidently. Scope of the innovative technological product: Operational risk assessment and early warning systems in banking practices for enhancing credit evaluation and decision-making processes. The product can be implemented within existing banking IT systems to support real-time credit scoring, loan monitoring, and portfolio risk simulations. It is especially useful in predictive risk management to detect early warning signs of potential loan defaults, thus enabling proactive intervention strategies.