The integration of open-source hardware and visual programming technologies provides an effective approach for developing contextualized STEM activities in higher education. This study presents the design, implementation, and functional verification of an educational prototype that combines customized electronic boards, sensors, and a visual programming interface developed in mBlock. The proposed platform integrates three educational boards—the Agriculture Board, Environment Board, and Aquaculture Board—for monitoring soil moisture, air-quality conditions, and water turbidity, respectively. The hardware implementation included the design and fabrication of customized printed circuit boards, followed by electrical continuity testing, power supply verification, and firmware execution. Customized programming blocks and communication extensions were then developed to enable interaction between the educational boards and the mBlock environment. Finally, the complete prototype was functionally verified during classroom implementation through contextualized STEM activities based on problem understanding, activity development, activity execution, and solution review. The results confirmed the correct operation of the hardware platform, successful communication between the educational boards and the visual programming environment, and real-time acquisition and visualization of sensor measurements. The integrated functional verification demonstrated correct interaction among the hardware platform, software extensions, connected sensors, and customized programming blocks under authentic classroom conditions. The proposed prototype provides a functional technological platform for implementing contextualized sensor-based STEM activities and demonstrates the feasibility of integrating open-source hardware and visual programming into higher education.

This comprehensive research article presents the empirical validation of a strategic maintenance engineering model through the systematic testing of 100 hypotheses (H1-H100) examining the complex interrelationships between external environmental factors, internal operational conditions, mediating results, and final organizational outcomes in physical asset maintenance management. The study employs a mixed-methods approach combining quantitative survey data from 124 organizations with qualitative inter-views, utilizing structural equation modeling and T-Student tests for hypothesis validation. The research investigates twelve dis-tinct relationship domains: (1) indirect external conditions and internal controls, (2) direct external conditions and internal controls, (3) internal mechanisms and controls, (4) indirect external conditions and mediating results, (5) direct external conditions and me-diating results, (6) internal conditions and mediating results, (7) mediating results between environment and internal, (8) mediating results and political management outcomes, (9) mediating results and operational outcomes, (10) political management and opera-tional outcomes, (11) final results, and (12) final results with control variables. The findings reveal that 78% of hypotheses were confirmed, demonstrating significant relationships between strategic maintenance management and organizational performance indicators including cost management, quality strategies, availability optimization, and overall productivity. The study proposes a five-stage strategic maintenance model: diagnostic analysis, strategic conditioning, implementation, evaluation, and strategic feedback. This research contributes to the theoretical development of maintenance engineering by integrating resource depen-dency theory, strategic management theory, and chaos theory into a unified framework for physical asset management. The im-plications extend to Industry 4.0 contexts, where IoT-based predictive maintenance and digital twin technologies are transforming traditional maintenance paradigms.