This study aims to underscore the relevance of pre-existing resilience experiences within communities affected by socio-political violence in Colombia, particularly in the context of developing effective risk management practices and enriching the CBDM model. This research employs a qualitative design, incorporating a multiple case study approach, which integrates a comprehensive literature review, in-depth interviews, and focus groups conducted in two Colombian communities, namely Salgar and La Primavera. The community of La Primavera effectively harnessed community empowerment and social support practices to confront socio-political violence, which evolved into a form of social capital that could be leveraged to address disaster risks. Conversely, in Salgar, individual and familial coping strategies took precedence. It is concluded that bolstering citizen participation in disaster risk management in both communities and governmental support for community projects aimed at reducing vulnerability is imperative. This study reveals that capabilities developed through coping with the humanitarian consequences of armed conflict, such as community empowerment and practices of solidarity and social support, can enhance community resilience in the face of disasters.

The rapid increase in the aging population has raised significant concerns about the living conditions and well-being of elderly residents in old communities. This study addresses these concerns by proposing a Sustainable Urban Renovation Assessment Model (SURAM) specifically designed to enhance elderly-friendly environments in Chongqing City. The model encompasses multiple dimensions, including the comfort of public facilities, service safety and convenience, medical travel services, infrastructure security, life service convenience, neighbor relations, ambulance aid accessibility, commercial service facilities, privacy protection, elderly care facilities and service supply, and medical and health facilities. By employing factor analysis, the study reduces the dimensionality of the 49 indicator factors, allowing for a more focused and comprehensive evaluation of the effectiveness of aging-friendly renovation efforts. The main factors identified in the proposed model include community infrastructure security, elderly comfort of community public facilities, completeness and convenience of surrounding living services, and security and convenience of elderly care services. The results reveal that the age-appropriate comfort of public facilities plays a significant role in achieving successful aging-appropriate renovation outcomes. The findings demonstrate that by addressing specific needs such as safety, accessibility, and convenience, communities can significantly improve the quality of life for elderly residents. Moreover, the application of SURAM provides actionable insights for policymakers, urban planners, and community stakeholders, guiding them in implementing targeted initiatives for sustainable and inclusive urban development.

Integrated risk value response is designed to reduce threats and increase opportunities, especially in terms of running the spun pile method innovation process in accordance with the ISO 56002:2019 standard. Implementing innovation can reduce risks and increase the competitiveness of the company. The method of making or producing spun piles is the research area examined in this study. Questionnaires were distributed to workers in precast concrete companies and most of them were involved in each spun pile production line in the company in order to identify the risk factors that existed in the production line for the spun pile manufacturing method. 30 respondents were workers from organizations in the positions of Director, Manager and Staff. The risk values and impacts are mapped for each dimension to the activity details and it is found that there are 5 high risks as dominant ones, mainly risks with codes R41, R10, R4, R37, and R36. Based on a survey, the highest risk of 30% was found in the stressing & spinning dimension, which is recommended for the innovation process. Innovation is conducted with 5 innovation processes, mainly identifying opportunities, creating concepts, validating concepts, developing solutions, and deploying solutions. Recommendations for improvements are made with preventive and corrective actions that must be taken from every aspect of the spun pile production method activities. Innovation recommendations are also proposed to monitor production activities in real-time utilizing existing information and communication technology. Handling of spun pile waste material must also be implemented with certain methods and produce products that add value for the company. Ultimately, to increase the company’s competitiveness by increasing assets, it is recommended to increase the company’s intangible assets. The company’s intangible assets encompass IPR ownership in the form of Patents and Copyrights.

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.