A finite element model for evaluating the effectiveness of the Advanced System for Implant Stability Testing (ASIST).

The Advanced system for Implant Stability Testing (ASIST) was developed to evaluate the stability of osseointegrated implants. ASIST matches the physical response with an analytical model's prediction to determine the stiffness of the bone implant interface (BII) which is then used to calculate the ASIST Stability Coefficient (ASC). In this investigation, a 3D dynamic finite element (FE) model of the ASIST experimental impact technique for bone anchored hearing aids was created. The objectives were to evaluate the analytical model's ability to capture the behavior of the implant system and to assess its effectiveness in minimising the effects of the system's geometry on the ASC scores. The models were developed on ABAQUS®, they consisted of the implant, abutment, screw, base support and impact rod. The models relied on frictional contact definitions between the system's components. The simplified "three-part" model had the implant, abutment and screw merged as one part while the "five-part" model treated them as separate components. Different interface conditions were simulated (friction coefficient range: 0-0.9) for three abutment lengths (6, 9 and 12 mm). The simulation output was the average nodal acceleration response of the rod, which was imported to the custom ASIST program in Mathematica® to obtain the ASC scores. The overall quality of the curve fits indicate that the analytical model is capable of representing the system's behavior. Moreover,ASC scores provide a reliable assessment of implant stability as they are sensitive to interface conditions and are minimally influenced by the system's geometry.

Disentangling Structural Confusion through Machine Learning: Structure Prediction and Polymorphism of Equiatomic Ternary Phases ABC.

A method to predict the crystal structure of equiatomic ternary compositions based only on the constituent elements was developed using cluster resolution feature selection (CR-FS) and support vector machine (SVM) classification. The supervised machine-learning model was first trained with 1037 individual compounds that adopt the most populated ternary 1:1:1 structure types (TiNiSi-, ZrNiAl-, PbFCl-, LiGaGe-, YPtAs-, UGeTe-, and LaPtSi-type) and then validated using an additional 519 compounds. The CR-FS algorithm improves class discrimination and indicates that 113 variables including size, electronegativity, number of valence electrons, and position on the periodic table (group number) influence the structure preference. The final model prediction sensitivity, specificity, and accuracy were 97.3%, 93.9%, and 96.9%, respectively, establishing that this method is capable of reliably predicting the crystal structure given only its composition. The power of CR-FS and SVM classification is further demonstrated by segregating the crystal structure of polymorphs, specifically to examine polymorphism in TiNiSi- and ZrNiAl-type structures. Analyzing 19 compositions that are experimentally reported in both structure types, this machine-learning model correctly identifies, with high confidence (>0.7), the low-temperature polymorph from its high-temperature form. Interestingly, machine learning also reveals that certain compositions cannot be clearly differentiated and lie in a "confused" region (0.3-0.7 confidence), suggesting that both polymorphs may be observed in a single sample at certain experimental conditions. The ensuing synthesis and characterization of TiFeP adopting both TiNiSi- and ZrNiAl-type structures in a single sample, even after long annealing times (3 months), validate the occurrence of the region of structural uncertainty predicted by machine learning.