Drilling and blasting are essential operations within the mining industry, playing a critical role in material fragmentation. Despite advancements in various blasting technologies, the process remains a dominant contributor to overall mining costs. Achieving cost efficiency requires the precise configuration of blast design parameters, including explosive charge quantity, to attain desired outcomes in fragmentation, ground vibrations, fly rock, and air over-pressure. This study introduces a novel artificial intelligence (AI)-driven model, XGBoost-PSO-T, which combines eXtreme Gradient Boosting (XGBoost) with Particle Swarm Optimization (PSO) through the integration of the Tri-Weight technique. The PSO-Tri-Weight method optimizes the hyperparameters of the XGBoost model, enhancing its predictive capabilities. The model's performance was evaluated using root mean square error (RMSE) and coefficient of determination (R²), with the results demonstrating that the XGBoost-PSO-T system outperforms the standard XGBoost approach, achieving an RMSE of 0.657 and an R² of 0.922. These findings suggest that the XGBoost-PSO-T model is a valuable tool for predicting fragmentation outcomes and optimizing blast designs in surface mining operations. The implementation of this system is recommended to improve blasting efficiency and reduce operational costs.

This study investigates the landscape dynamics and management practices affecting the Association for Forestry and Environmental Education (AFEE) forest massif, located in the monomodal agroecological zone of Cameroon. Using remote sensing data, including Landsat 8 imagery from 2014, 2019, and 2024, in conjunction with field observations, the spatio-temporal changes in land use over the past decade were mapped. Additionally, interviews were conducted with 30 local residents selected through snowball sampling to assess their perceptions of the forest's degradation and the impact of their livelihood activities on the surrounding environment. The results reveal a significant decline in the forest's ecological integrity, with the AFEE massif, originally covered entirely by mature secondary forest in 2014 (200 ha), experiencing a 77.7% reduction in forest cover by 2024. This loss has been primarily replaced by anthropogenic land uses, including young secondary forests (22.9%, 45.95 ha), swamps (17.6%, 35.35 ha), fallow lands (16.8%, 33.7 ha), rubber and oil palm plantations (1.46%, 2.91 ha), and agricultural plots (18.7%, 37.48 ha). Activities such as agriculture, hunting, artisanal sawmilling, and fishing, although central to the livelihoods of local people, have contributed significantly to the degradation of the natural landscape. These practices, while essential for local economic well-being, have negatively impacted the forest ecosystem. Given the critical role of the AFEE massif in environmental education, these findings are essential for the development of strategies that can balance the conservation of natural ecosystems with the socio-economic needs of local populations. The results underscore the need for integrated management approaches that promote both environmental preservation and sustainable livelihoods to ensure the continued provision of ecosystem services for future generations.