Yojana J. P. Carreón

Dried droplet imaging has become an established approach for analyzing complex fluid systems as evaporation patterns preserve physicochemical and structural information. However, spatial variability of these patterns limits the effectiveness of conventional texture descriptors. This work presents a region-aware deep learning algorithm for the classification of dried droplets through spatial decomposition and convolutional learning. Dried droplet images were geometrically standardized prior to patch extraction. The Hough transform supported consistent crown–core localization and patch extraction across the dataset. Each image was divided into twenty localized regions. Twelve represented peripheral patches sampled at 30° intervals. The remaining eight patches were extracted from four central regions using two complementary angular orientations (30° and 90°) per region. This process allowed dataset augmentation and evaluation of regional contributions. Patches were processed using the VGG16 convolutional neural network, following repeated patch-level and droplet-level partitioning strategies designed to prevent data leakage. The proposed algorithm was evaluated on Methotrexate (MTX) droplets prepared with 40%, 60%, and 80% water dilution levels relative to a reference solution. Results show that the proposed strategy increases classification accuracy, outperforming traditional descriptors. These findings demonstrate that spatial decomposition combined with convolutional learning constitutes an effective approach for heterogeneous evaporative pattern classification.

Foliar fertilization, an effective agricultural practice, involves the application of nutrients directly through droplets on plant leaves. The mechanisms of mass transport and deposition that arise from the drying of a drop determine the distribution of mass on a surface. Understanding these processes is crucial for optimizing foliar fertilization, ensuring even nutrient distribution, and improving crop yields and quality. This study experimentally investigates deposit formation from the evaporation of fertilizer droplets in various configurations: sessile, vertical, and pendant. We explored the effects of initial droplet volume, vapor pressure, and sorbitol presence on the final deposit morphology. The results reveal distinctive morphological patterns. Sessile drops exhibit two types of deposits—central crystal accumulation with fibrous structures or entirely fibrous structures. In contrast, vertical drops display two zones—fibrous structures at the bottom and small aggregates at the top. On the other hand, pendant drops predominantly feature intertwined crystals with peripheral fibrous structures. We found that high vapor pressures (RH = 60%) inhibit deposit formation within 72 h. Furthermore, the study measures relative evaporation time, showing that sessile droplets exhibit the longest evaporation times, followed by vertical and pendant droplets. Texture analysis, based on GLCM entropy, reveals that deposits generated under low vapor pressure (RH = 20%) show no significant differences in their entropy values, regardless of the droplet configuration and its initial volume. However, at intermediate vapor pressure (RH = 40%), entropy values vary significantly with droplet volume and configuration, being higher in sessile drops and lower in vertical ones. Additionally, we investigated the impact of sorbitol on the coating of sessile fertilizer droplets. We find that configurational entropy decreases exponentially with sorbitol concentration, inducing a morphological transition from fibrous structures to dispersed small aggregates. These findings highlight the complexity of pattern formation in fertilizer deposits and their potential implications for optimizing surface coating processes.

Hyperlipidemia is the elevated concentration of lipids in the blood, and it increases the probability of arterial obstruction, infarctions, and other complications of the circulatory system. While there are indications that qualitative analysis of blood stains could potentially identify patients with this pathology, the efficacy of this method remains uncertain. In this paper, we report an experimental study that investigates the formation of patterns in dried blood droplets with varying concentrations of ultrapure water. Two blood samples, one healthy and one with moderate hyperlipidemia, were examined to determine the ideal water and blood mixtures for detecting high lipid concentrations. Numerous intricate patterns were observed throughout the central region and periphery of the dried droplet. These patterns encompass various forms, such as plaques, bump-like patterns, and a range of cracks including random, radial, and ortho-radial configurations. By calculating the entropy of the Gray Level Co-occurrence Matrix (GLCM) and analyzing ROC curves, we determined that solutions with 4% and 12% hematocrit (indicating a high percentage of ultrapure water) exhibit over 95% accuracy in differentiating high lipid concentrations. These findings provide a promising outlook for the development of diagnostic methods based on the study of diluted blood coatings.