One of the first decisions to be made when building a mineral resource model is the definition of geological/geostatistical domains. Cluster analysis is a set of techniques in machine learning that can be especially suited for this matter. In order to compare different approaches, two clustering algorithms were investigated in this study: k-means and the dual-space clustering algorithm. Choosing the most appropriate method and the number of clusters can be challenging and some metrics are needed to support these decisions, including the validation of the spatial distribution of the clusters, which is not always appropriately discussed in the literature. We introduce the use of correlograms of the indicators for that matter. Although clustering techniques can be robust for an application in resource modelling, expert knowledge is still necessary when applying cluster analysis to resource modeling, since final decisions should not be based solely on statistical indexes, but also on experience. In this paper, the proposed methodology was tested in a three-dimensional dataset related to a phosphate/titanium deposit.

This study compares the performance of three YOLO-based object detection models—YOLOv3, YOLOv5, and YOLOv8—for vehicle detection and classification at an urban intersection in Montería, Colombia. Recordings from five consecutive days, spanning three time slots, were used, totaling approximately 135,000 frames with variability in lighting and weather conditions. Frames were preprocessed by maintaining the aspect ratio and were normalized according to each model. The evaluation employed models pre-trained on COCO, without fine-tuning, enabling an objective assessment of their generalization capacity. Precision, recall, F1-score, and mAP@0.5 were computed globally and by vehicle class. YOLOv5 achieved the best balance between precision and recall (F1-score = 0.78) and the highest mAP (0.63), while YOLOv3 showed lower recall and mAP, and YOLOv8 performed competitively but slightly below YOLOv5. Cars and motorcycles were the most robust classes, whereas bicycles and trucks showed greater detection challenges. Visual evaluation confirmed stable performance on cloudy days and in light rain, with reduced accuracy under sunny conditions with high contrast. These findings highlight the potential of modern YOLO architectures for intelligent urban traffic monitoring and management. The generated dataset constitutes a replicable resource for future mobility research in similar contexts.

One of the key objectives in short-term mine planning is to establish a sequence of contiguous blocks organized into diglines, each characterized by consistent low-grade variability and minimal fluctuations, ensuring a stable output. This practice guarantees a steady and reliable supply of ore to the processing plant across various operational phases, each phase outlined for clarity. The creation of this digline requires the generation of multiple random models and involves choosing a model whose grade histogram aligns closely with the predefined criteria. In stochastic mine planning, geological uncertainty and transfer functions are employed to propagate the risk into the mining schedule focusing on grade control. This is accomplished by opting for blocks that present the lowest risk associated with grade uncertainty, a process enhanced by prioritizing blocks situated in geopositions with a proven high probability of occurrence. This study unveils detailed simulation models, clearly outlined digline selection methodologies, and a thorough optimization process, each aspect exemplified within the practical context of a phosphate mine. Our findings confirm the viability of this approach in real industry practice.