This research presents a novel approach utilizing a self-enhanced chimp optimization algorithm (COA) for feature selection in crowdfunding success prediction models, which offers significant improvements over existing methods. By focusing on reducing feature redundancy and improving prediction accuracy, this study introduces an innovative technique that enhances the efficiency of machine learning models used in crowdfunding. The results from this study could have a meaningful impact on how crowdfunding campaigns are designed and evaluated, offering new strategies for creators and investors to increase the likelihood of campaign success in a rapidly evolving digital funding landscape.

Lack of knowledge, attitude, and behavior in managing leftover foods in households impacts the natural ecosystem and food chain, particularly in developing countries. This research aims to analyze appropriate methods for reducing and processing food waste produced in household areas. This research method uses qualitative research with operational research methods carried out for 6 months on 25 housewives in Pondok Labu Village in South Jakarta, Indonesia. The research was carried out in 3 stages, the first stage before the intervention, the second stage providing the intervention, and the third stage after the intervention. Results showed that before the intervention, on average each respondent produced 351 g of food waste each day. This amount decreased to 8.43 g/day after respondents participated in socialization to reduce food waste and training to manage food waste. The concluded that a combination of education and training improves knowledge, attitude, and behavior in household food waste management and helps moderate food waste generation.

This study focuses on the effectiveness of systematic approaches to achieve business success, through integrated digital marketing strategies with the use of mobile applications. Focused on contemporary digital markets, the research highlights the transformative potential of these strategies about improving the quality of services and products, while promoting business sustainability. The objective of this research is to develop and evaluate a mobile application, designed to optimize customer orders within communities on the move. Through a mixed approach that includes semi-structured interviews with community members and digital marketing experts, along with quantitative surveys, assessed user perceptions and effectiveness of the app. The results indicate great acceptance and effectiveness of this digital tool, facilitating direct interactions, and improving accessibility to the service despite physical and digital limitations, reducing digital gaps and promoting economic empowerment among marginalized communities.

This paper presents a practical approach to empowering software entrepreneurship in Saudi Arabia through a unique course offered by the Software Engineering department at Prince Sultan University. The course, SE495 Emergent Topics in Software Engineering: Software Entrepreneurship, combines software engineering and entrepreneurship to equip students with the necessary skills to develop innovative software solutions that solve real-world problems. The course covers a range of topics, including platform development, market research, and pitching to investors, and features guest speakers from the industry. By the end of the course, students will have gained a deep understanding of the software development process and its intersection with entrepreneurship and will be able to develop a working prototype of a software solution that solves a real-world problem. The course’s practical approach ensures that students are well-prepared to navigate the complexities of the digital and software sectors and succeed in an ever-changing business landscape.

This study introduces an innovative approach to assessing seismic risks and urban vulnerabilities in Nador, a coastal city in northeastern Morocco at the convergence of the African and Eurasian tectonic plates. By integrating advanced spatial datasets, including Landsat 8–9 OLI imagery, Digital Elevation Models (DEM), and seismic intensity metrics, the research develops a robust urban vulnerability index model. This model incorporates urban land cover dynamics, topography, and seismic activity to identify high-risk zones. The application of Landsat 8–9 OLI data enables precise monitoring of urban expansion and environmental changes, while DEM analysis reveals critical topographical factors, such as slope instability, contributing to landslide susceptibility. Seismic intensity metrics further enhance the model by quantifying earthquake risk based on historical event frequency and magnitude. The calculation based on higher density in urban areas, allowing for a more accurate representation of seismic vulnerability in densely populated areas. The modeling of seismic intensity reveals that the most susceptible impact area is located in the southern part of Nador, where approximately 50% of the urban surface covering 1780.5 hectares is at significant risk of earthquake disaster due to vulnerable geological formations, such as unconsolidated sediments. While the findings provide valuable insights into urban vulnerabilities, some uncertainties remain, particularly due to the reliance on historical seismic data and the resolution of spatial datasets, which may limit the precision of risk estimations in less densely populated areas. Additionally, future urban expansion and environmental changes could alter vulnerability patterns, underscoring the need for continuous monitoring and model refinement. Nonetheless, this research offers actionable recommendations for local policymakers to enhance urban planning, enforce earthquake-resistant building codes, and establish early warning systems. The methodology also contributes to the global discourse on urban resilience in seismically active regions, offering a transferable framework for assessing vulnerability in other coastal cities with similar tectonic risks.