The six core competencies of mathematics in vocational schools are becoming increasingly important in mathematics learning. The reverse teaching design of vocational school mathematics, which focuses on core competencies, precisely grasps the internal logic of knowledge from a holistic perspective, and designs it on a unit by unit basis. The design process is to infer the starting point from the endpoint. Therefore, how to use reverse thinking in teaching design research in vocational school mathematics teaching under the background of core competencies will be the main content of this article.

This study aims to explore the implications of imported electrical equipment in Indonesia, analysing both short-term and long-term impacts using a quantitative approach. The research focuses on understanding how various economic factors, such as domestic production, international pricing, national income, and exchange rates, influence the country’s import dynamics in the electrical equipment sector. Employing an Error Correction Model (ECM) for regression analysis, the study utilises time-series data from 2007 to 2021 to delve into the complex interplay of these variables. The methodology involves a comprehensive analysis using the Augmented Dickey-Fuller and Phillips-Perron tests to assess the stationarity of the data. This approach ensures the robustness of the ECM, which is employed to analyse the short-term and long-term effects of the identified variables on electrical equipment imports in Indonesia. The results reveal significant relationships between these economic factors and import levels. In the short term, imports are shown to be sensitive to changes in domestic economic conditions and international market prices, while in the long term, the country’s economic growth, reflected through GDP, emerges as a significant determinant. The findings suggest that Indonesia’s electrical equipment import policies must adapt highly to domestic and international economic changes. In the short term, a responsive approach is required to manage the immediate impacts of market fluctuations. The study highlights the importance of aligning import strategies with broader economic growth and environmental sustainability goals for long-term sustainability. Policymakers are advised to focus on enhancing domestic production capabilities, reducing import dependency, and ensuring that environmental considerations are integral to import policies. This study contributes to understanding import dynamics in a developing country context, offering valuable insights for policymakers and industry stakeholders in shaping strategies for economic growth and sustainability in the electrical equipment sector. The findings underscore the need for a balanced, data-driven approach to managing imports, aligning short-term responses with long-term strategic objectives for Indonesia’s ongoing development and industrial advancement.

The effects of climate change are recognized globally. This study hypothesizes that climate change impacts are a complex system that creates a ripple effect on water security, food security, and economic security. Ultimately, those domains simultaneously exacerbate climate change effects and produce national security concerns. The study’s framework uses a transdisciplinary team’s quantitative and qualitative approach to evaluate the challenges and possible solutions to climate change security on the Water–Food–Socioeconomic Nexus. Iraq has been taken as a case study highlighting the deficits in management and governance. The dynamic of the ripple effect shows the interventions for each sector’s water-food-socioeconomic and security that collectively impact upon each other over time. The radical shift in the political infrastructure after 2003 from a centralized to a decentralized one without proper preparation is one of the root causes of the governance and management anarchy. About 228 state and non-state actors are involved in decision-making, leaving it fragile and unsustainable. Only 1% of the national budget is allocated to both the Ministry of Water Resources and the Ministry of Agriculture, which leaves no capacity to mitigate the risk of climate change impact.

In the agricultural sector of Huila, particularly among SMEs in coffee, cocoa, fish, and rice subsectors, the transition to the International Financial Reporting Standards (IFRS) is paramount yet challenging. This research aims to offer management guidelines to support Huila’s agricultural SMEs in their IFRS transition, underpinning the region’s aspirations for financial standardization and economic advancement. Utilizing a mixed-methods managerial approach, data was gathered from 13 representative companies using validated questionnaires, interviews, and analyzed with SPSS and ATLAS.ti. Results indicate that while there is evident progress in IFRS adoption, 12 out of 13 firms adopted IFRS, with rice leading in terms of adoption duration. While 77% found IFRS useful for financial statements, half reported insufficient staff training. The transition highlighted challenges, including asset recognition and valuation, and emphasized enhancing institutional support and IFRS training. Interviews revealed managerial commitment and expertise as significant factors. Recommendations for successful implementation include leadership involvement, continuous professional development, anticipating costs, clear accounting policies, and meticulous record-keeping. The study concludes that adopting IFRS enhances financial reporting quality, urging entities to converge their reporting practices without hesitation for improved comparability, relevance, and reliability in their financial disclosures.

The present study focuses on improving Cognitive Radio Networks (CRNs) based on applying machine learning to spectrum sensing in remote learning scenarios. Remote education requires connection dependability and continuity that can be affected by the scarcity of the amount of usable spectrum and suboptimal spectrum usage. The solution for the proposed problem utilizes deep learning approaches, namely CNN and LSTM networks, to enhance the spectrum detection probability (92% detection accuracy) and consequently reduce the number of false alarms (5% false alarm rate) to maximize spectrum utilization efficiency. By developing the cooperative spectrum sensing where many users share their data, the system makes detection more reliable and energy-saving (achieving 92% energy efficiency) which is crucial for sustaining stable connections in educational scenarios. This approach addresses critical challenges in remote education by ensuring scalability across diverse network conditions and maintaining performance on resource-constrained devices like tablets and IoT sensors. Combining CRNs with new technologies like IoT and 5G improves their capabilities and allows these networks to meet the constantly changing loads of distant educational systems. This approach presents another prospect to spectrum management dilemmas in that education delivery needs are met optimally from any STI irrespective of the availability of resources in the locale. The results show that together with machine learning, CRNs can be considered a viable path to improving the networks' performance in the context of remote learning and advancing the future of education in the digital environment. This work also focuses on how machine learning has enabled the enhancement of CRNs for education and provides robust solutions that can meet the increasing needs of online learning.