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Multi-material 3D printing combines the functional properties of different materials (e.g., mechanical, electrical, color) within a single object that is fabricated without manual assembly. However, this presents sustainability challenges as multi-material objects cannot be easily recycled. Because each material has a different processing temperature, considerable effort must be used to separate them for recycling. This paper presents a computational fabrication technique to generate dissolvable interfaces between different materials in a 3D printed object without affecting the object’s intended use. When the interfaces are dissolved, the object is disassembled to enable recycling of the individual materials. We describe the computational design of these interfaces alongside experimental evaluations of their strength and water solubility. Finally, we demonstrate our technique across 9 multi-material 3D printed objects of varying structural and functional complexity. Our technique enables us to recycle 89.97% of the total mass of these objects, promoting greater sustainability in 3D printing. 

In this paper, we propose differentially private algorithms for robust (multivariate) mean estimation and inference under heavy-tailed distributions, with a focus on Gaussian differential privacy. First, we provide a comprehensive analysis of the Huber mean estimator with increasing dimensions, including non-asymptotic deviation bound, Bahadur representation, and (uniform) Gaussian approximations. Secondly, we privatize the Huber mean estimator via noisy gradient descent, which is proven to achieve near-optimal statistical guarantees. The key is to characterize quantitatively the trade-off between statistical accuracy, degree of robustness and privacy level, governed by a carefully chosen robustification parameter. Finally, we construct private confidence intervals for the proposed estimator by incorporating a private and robust covariance estimator. Our findings are demonstrated by simulation studies. 

The data-driven newsvendor problem with features has recently emerged as a significant area of research, driven by the proliferation of data across various sectors such as retail, supply chains, e-commerce, and healthcare. Given the sensitive nature of customer or organizational data often used in feature-based analysis, it is crucial to ensure individual privacy to uphold trust and confidence. Despite its importance, privacy preservation in the context of inventory planning remains unexplored. A key challenge is the nonsmoothness of the newsvendor loss function, which sets it apart from existing work on privacy-preserving algorithms in other settings. This paper introduces a novel approach to estimating a privacy-preserving optimal inventory policy within the f-differential privacy framework, an extension of the classical [Formula: see text]-differential privacy with several appealing properties. We develop a clipped noisy gradient descent algorithm based on convolution smoothing for optimal inventory estimation to simultaneously address three main challenges: (i) unknown demand distribution and nonsmooth loss function, (ii) provable privacy guarantees for individual-level data, and (iii) desirable statistical precision. We derive finite-sample high-probability bounds for optimal policy parameter estimation and regret analysis. By leveraging the structure of the newsvendor problem, we attain a faster excess population risk bound compared with that obtained from an indiscriminate application of existing results for general nonsmooth convex loss. Our bound aligns with that for strongly convex and smooth loss function. Our numerical experiments demonstrate that the proposed new method can achieve desirable privacy protection with a marginal increase in cost. This paper was accepted by J. George Shanthikumar, data science. Funding: This work was supported by the National Science Foundation [Grants DMS-2113409 and DMS 2401268 to W.-X. Zhou, and FRGMS-1952373 to L. Wang]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.01268 . 

The Biofibers Spinning Machine produces bio-based fibers (biofibers) that are dissolvable and biodegradable. These fibers enable recycling of smart textiles by making it easy to separate electronics from textiles. Currently, prototyping with the machine requires the use of low-level commands, i.e. G-code. To enable more people to participate in the sustainable smart textiles design space and develop new biofiber materials, we need to provide accessible tools and workflows. This work explores a software tool that facilitates material exploration with machine parameters. We describe the interface design and demonstrate using the tool to quantify the relationship between machine parameters and spun gelatin biofibers.