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Additive manufacturing (AM), particularly Laser Powder Bed Fusion (L-PBF), holds the potential for producing high-quality parts with intricate details. However, optimizing process parameters for arbitrary alloy chemistries to ensure printability remains challenging. This study evaluates machine learning (ML) models to predict a material’s amenability to L-PBF via the printability index, focusing on High Entropy Alloy (HEA) spaces. The printability index of a material is defined as the percentage of the defect-free L-PBF processing window. Our study revealed that CatBoost Regressors and Random Forest Regressors excel in predictive accuracy, consistently yielding predictions with competitive error metrics such as the mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and . In addition, competitive rank-order coefficients such as Spearman and Kendall-tau demonstrated that these models are not overfitting. Feature importance analysis using Shapley Additive Explanations (SHAP) highlighted key material properties influencing printability, such as kinetic viscosity, average Pauling electronegativity, and electric conductivity. While both models performed comparably in predictive accuracy, the Random Forest Regressor demonstrated superior computational efficiency, particularly with large datasets. Robustness tests confirmed its reliability across different test sizes. This research underscores the importance of considering factors like computational efficiency, interpretability, and robustness to noise when selecting ML models for L-PBF material printability prediction. Leveraging Integrated Computational Materials Engineering (ICME) methodologies and ML models can significantly optimize process parameters and material properties, paving the way for innovative solutions in L-PBF. This approach accelerates the assessment of new materials and optimizes existing ones for L-PBF processes, contributing significantly to the field of AM. 

Secret sharing (SS) is a foundational cryptographic primitive with diverse applications, including secure multiparty computation and conditional disclosure of secrets. While traditional schemes have primarily emphasized information-theoretic security, recent advancements have increasingly leveraged computational assumptions to achieve more efficient constructions and support broader access policies. Despite these successes, most existing computational secret sharing (CSS) schemes are limited to a static security model, where adversaries must commit to their choice of corrupted participants at the outset. A critical challenge in CSS lies in achieving adaptive security, where adversaries can dynamically select participants to corrupt, better reflecting real-world threat models. In this paper, we present a novel transformation that converts any statically secure CSS scheme into an adaptively secure one while preserving the original access policy and computational assumptions, providing a framework for bridging the gap between static and adaptive security. Our construction introduces a multiplicative share size overhead of where is the number of parties. Additionally, we explore trade-offs in efficiency and security, offering more efficient adaptive CSS constructions for specific, restricted policy classes. This work addresses key limitations in the current landscape of CSS and paves the way for broader adoption of adaptively secure secret sharing in cryptographic applications.