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Functionally graded materials (FGMs) enable applications in fields such as biomedicine and architecture, but their fabrication suffers from shortcomings in gradient continuity, interfacial bonding, and directional freedom. In addition, most commercial design software fail to incorporate property gradient data, hindering explorations of the design space of FGMs. Here, we leveraged a combined approach of materials engineering and digital processing to enable extrusion-based multimaterial additive manufacturing of cellulose-based tunable viscoelastic materials with continuous, high-contrast, and multidirectional stiffness gradients. A method to engineer sets of cellulose-based materials with similar compositions, yet distinct mechanical and rheological properties, was established. In parallel, a digital workflow was developed to embed gradient information into design models with integrated fabrication path planning. The payoff of integrating these physical and digital tools is the ability to achieve the same stiffness gradient in multiple ways, opening design possibilities previously limited by the rigid coupling of material and geometry., German Research Foundation (DFG); Max Planck Society; University of Stuttgart

This study provides an explanatory model for the takeover selection process in Malaysia using 144 non-fmancial firms, which includes bidders, targets, control bidders and control targets for period 1980-1993. The takeover firms are first analyzed on a univariate basis to assess the differences in the bidder and target groups to examine if the bidder firms were more efficient than the target firms were. The discriminant model that provides a useful tool for explaining the categorical classification is then used for delineating the bidder and target firms. A set of five economic/financial variables has been identified to discriminate between the firm’s groupings using publicly available time series data. The empirical findings suggest that; a) five predictive variables account for about 90% of the firms’ groupings, b) fmancial leverage is the most powerful discriminatory variable followed by profit, risk, size, and growth, in that order, c) bidder firms have higher profit and growth, and lower leverage, risk and size, than the target firms, and accordingly provide some support that, d) the takeover was motivated by the bidder firms’ desire for reaping the fruits of economies of scale in order to maintain the tempo of high profit and high growth and/or for displacement of inefficient managers of target firms. The accuracy of the results is shown to hold using the logistic regression model.