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Abstract Objective. The insertion of penetrating neural probes into the brain is crucial for advancing neuroscience, yet it involves various inherent risks. Prototype probes are typically inserted into hydrogel-based brain phantoms and the mechanical responses are analyzed in order to inform the insertion mechanics duringin vivoimplantation. However, the underlying mechanism of the insertion dynamics of neural probes in hydrogel brain phantoms, particularly the phenomenon of cracking, remains insufficiently understood. This knowledge gap leads to misinterpretations and discrepancies when comparing results obtained from phantom studies to those observed under thein vivoconditions. This study aims to elucidate the impact of probe sharpness and dimensions on the cracking mechanisms and insertion dynamics characterized during the insertion of probes in hydrogel phantoms.Approach. The insertion of dummy probes with different shank shapes defined by the tip angle, width, and thickness is systematically studied. The insertion-induced cracks in the transparent hydrogel were accentuated by an immiscible dye, tracked byin situimaging, and the corresponding insertion force was recorded. Three-dimensional finite element analysis models were developed to obtain the contact stress between the probe tip and the phantom.Main results. The findings reveal a dual pattern: for sharp, slender probes, the insertion forces remain consistently low during the insertion process, owing to continuously propagating straight cracks that align with the insertion direction. In contrast, blunt, thick probes induce large forces that increase rapidly with escalating insertion depth, mainly due to the formation of branched crack with a conical cracking surface, and the subsequent internal compression. This interpretation challenges the traditional understanding that neglects the difference in the cracking modes and regards increased frictional force as the sole factor contributing to higher insertion forces. The critical probe sharpness factors separating straight and branched cracking is identified experimentally, and a preliminary explanation of the transition between the two cracking modes is derived from three-dimensional finite element analysis.Significance. This study presents, for the first time, the mechanism underlying two distinct cracking modes during the insertion of neural probes into hydrogel brain phantoms. The correlations between the cracking modes and the insertion force dynamics, as well as the effects of the probe sharpness were established, offering insights into the design of neural probes via phantom studies and informing future investigations into cracking phenomena in brain tissue during probe implantations. 

Quantification of all types of uncertainty helps to establish reliability in any analysis. This research focuses on uncertainty in two attribute levels of wetland classification and creates visualization tools to guide analysis of spatial uncertainty patterns over several scales. A novel variant of confusion matrix analysis compares the Cowardin and Hydrogeomorphic wetland classification systems, identifying areas and types of misclassification for binary and multivariate categories. The specific focus on uncertainty in the paper refers to categorical consistency, that is, agreement between the two classification systems, rather than comparing observed data to ground truth. Consistency is quantified using confusion matrix analysis. Aggregation across progressive focal windows transforms the confusion matrix into a multiscale data pyramid for quick determination of where attribute uncertainty is highly variant, and at what spatial resolutions classification inconsistencies emerge. The focal pyramids summarize precision, recall, and F1 scores to visualize classification differences across spatial scales. Findings show that the F1 scores appear most informative on agreement about wetlands misclassification at both coarse and fine attribute scales. The pyramid organizes multi-scale uncertainty in a single unified framework and can be “sliced” to view individual focal levels of attribute consistency. Results demonstrate how the confusion matrix can be used to quantify the percentage of a study area in which inconsistencies occur reflecting wetland presence and type. The research provides confusion metrics and display tools to focus attention on specific areas of large data sets where attribute uncertainty patterns may be complex, thus reducing land managers’ workloads by highlighting areas of uncertainty where field checking might be appropriate, and improving analytics by providing visualization tools to quickly see where such areas occur.