Ideological and political education is not only a basic course to cultivate students’ moral quality, but also an important part of modern education outline. The current intelligent electronic technology course should strengthen the gradual integration of curriculum and ideological education. Under the background of the new era, the state pays more attention to education, aiming to integrate the concept of ideological and political education into the classroom to effectively improve the effectiveness of comprehensive education for students. The course of intelligent electronic technology should integrate ideological and political education resources and innovate educational means from teachers to classrooms. This paper analyzes the principle of Integrating ideological and political education into intelligent electronic technology curriculum, and hopes to put forward constructive suggestions on the research and innovation path.

This research can help improve public health and ensure the sustainable transformation of the food system. This study aims to analyze the success of Regional Food Security development activities through Community Empowerment with the food independent village program carried out by regional command units in the ranks of Korem 063/SGJ (Sunan Gunung Jati). This study uses qualitative descriptive with comparative methods. Population includes villages that have received the food independent village program in West Java (Kuningan, Cirebon, Majalengka, and Cirebon City) between 2009 and 2022. The research sample consisted of 4 villages selected from each of the districts/cities. The research informants totalled 37 people, consisting of stakeholders from the Korem 063/Sunan Gunung Jati Unit and its staff, the Food Security Service, village heads, affinity groups or farmers, and community leaders in the research area. The results of the study indicate that the success and failure in the implementation of the food independent village program by affinity groups and the food security development activity program by Satkowil have an effect on food availability, food distribution and food consumption. This research is expected to provide a comprehensive overview of the implementation of the food independent village program and food security development activities by regional command units in West Java.

Optimizing Storage Location Assignment (SLA) is essential for improving warehouse operations, reducing operational costs, travel distances and picking times. The effectiveness of the optimization process should be evaluated. This study introduces a novel, generalized objective function tailored to optimize SLA through integration with a Genetic Algorithm. The method incorporates key parameters such as item order frequency, storage grouping, and proximity of items frequently ordered together. Using simulation tools, this research models a picker-to-part system in a warehouse environment characterized by complex storage constraints, varying item demands and family-grouping criteria. The study explores four scenarios with distinct parameter weightings to analyze their impact on SLA. Contrary to other research that focuses on frequency-based assignment, this article presents a novel framework for designing SLA using key parameters. The study proves that it is advantageous to deviate from a frequency-based assignment, as considering other key parameters to determine the layout can lead to more favorable operations. The findings reveal that adjusting the parameter weightings enables effective SLA customization based on warehouse operational characteristics. Scenario-based analyses demonstrated significant reductions in travel distances during order picking tasks, particularly in scenarios prioritizing ordered-together proximity and group storage. Visual layouts and picking route evaluations highlighted the benefits of balancing frequency-based arrangements with grouping strategies. The study validates the utility of a tailored generalized objective function for SLA optimization. Scenario-based evaluations underscore the importance of fine-tuning SLA strategies to align with specific operational demands, paving the way for more efficient order picking and overall warehouse management.

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.