The inmates are running the asylum

Metamaterials exhibit properties beyond those exhibited by conventional materials in conventional scenarios. These have been investigated both theoretically and experimentally at length. In many cases the underpinning physical understanding of metamaterials has greatly preceded our ability to manufacture constituent structures. However, the development of additive manufacturing techniques gives new possibilities for the fabrication of complex metamaterial structures, many of which cannot be realised through conventional fabrication methods. The literature to date contains contributions from a diverse group of researchers from the physical sciences, mathematics, and manufacturing technology in the creation of metamaterials for electromagnetic, acoustic and mechanical applications. It is proposed that additive manufacturing holds the key to realise the capabilities of this vibrant research community and permit the creation of new paradigms in fundamental structures but also exploitation through application. For this purpose, a literature review is presented which identifies key advances in metamaterials alongside additive manufacturing and proposes new opportunities for researchers to work together through intra/inter disciplinary research to realise structures which exhibit extraordinary behaviour(s). This review represents a comprehensive account of the state-of-the-art in the production of such metamaterials using additive manufacturing methods and highlights areas, which, based on trends observed in the literature, are worthy of further research and require a coordinated effort on behalf of the afore mentioned disciplines in order to advance the state-of-the-art.

Additive manufacturing of metamaterials: A review

Future of additive manufacturing: Overview of 4D and 3D printed smart and advanced materials and their applications

Digital fabrication courses that relied on physical makerspaces were severely disrupted by COVID-19. As universities shut down in Spring 2020, instructors developed new models for digital fabrication at a distance. Through interviews with faculty and students and examination of course materials, we recount the experiences of eight remote digital fabrication courses. We found that learning with hobbyist equipment and online social networks could emulate using industrial equipment in shared workshops. Furthermore, at-home digital fabrication offered unique learning opportunities including more iteration, machine tuning, and maintenance. These opportunities depended on new forms of labor and varied based on student living situations. Our findings have implications for remote and in-person digital fabrication instruction. They indicate how access to tools was important, but not as critical as providing opportunities for iteration; they show how remote fabrication exacerbated student inequities; and they suggest strategies for evaluating trade-offs in remote fabrication models with respect to learning objectives.

Remote Learners, Home Makers: How Digital Fabrication Was Taught Online During a Pandemic

Due to the wide diffusion of 3D printing technologies, geometric algorithms for Additive Manufacturing are being invented at an impressive speed. Each single step, in particular along the Process Planning pipeline, can now count on dozens of methods that prepare the 3D model for fabrication, while analysing and optimizing geometry and machine instructions for various objectives. This report provides a classification of this huge state of the art, and elicits the relation between each single algorithm and a list of desirable objectives during Process Planning. The objectives themselves are listed and discussed, along with possible needs for tradeoffs. Additive Manufacturing technologies are broadly categorized to explicitly relate classes of devices and supported features. Finally, this report offers an analysis of the state of the art while discussing open and challenging problems from both an academic and an industrial perspective.

From 3D Models to 3D Prints: an Overview of the Processing Pipeline

This chapter also proposes a solution to the compilation of biochemical applications on a given biochip architecture. The compilation problem proposed in the previous chapter has assumed that reconfigurable operations are performed inside rectangular modules whose location and shape remain fixed throughout the execution of operations. However, as discussed in Sect. 3.4, reconfigurable operations can be performed anywhere on the array, by simply routing the corresponding droplets on a sequence of electrodes. In this chapter, we propose two models for operation execution inside virtual devices, which take into consideration the reconfigurability of microfluidic operations: (1) moving a module during the operation execution and (2) changing the shape of the device on which an operation is bound during its execution. These operation execution models aim at reducing the fragmentation of the free space on the microfluidic array during the placement step of the compilation process. In this context, we revisit the compilation problem and present an extension to the Tabu Search metaheuristic optimization solution introduced earlier, with a focus on a new dynamic module placement algorithm. The advantages of the new operation execution models are evaluated using extensive experiments.

Module-Based Compilation with Reconfigurable Operation Execution

When Embedded Systems Meet Life Sciences: Microfluidic Biochips For Real-Time Healthcare

Microfluidic-based biochips are replacing the conventional biochemical analyzers, by integrating all the necessary functions for biochemical analysis using microfluidics. The digital microfluidic biochips (DMBs) manipulate discrete amounts of fluids of nanoliter volume, named droplets, on an array of electrodes to perform operations such as dispensing, transport, mixing, split, dilution and detection. Researchers have proposed compilation approaches, which, starting from a biochemical application and a biochip architecture, determine the allocation, resource binding, scheduling, placement and routing of the operations in the application. During the execution of a bioassay, operations could experience transient faults, thus impacting negatively the correctness of the application. We have proposed both offline (design time) and online (runtime) recovery strategies. The online recovery strategy decides the introduction of the redundancy required for fault-tolerance. We consider both time redundancy, i.e., re-executing erroneous operations, and space redundancy, i.e., creating redundant droplets for fault-tolerance. Error recovery is performed such that the number of transient faults tolerated is maximized and the timing constraints of the biochemical application are satisfied. Previous work has assumed that the biochip architecture is given, and most approaches consider a rectangular shape for the electrode array, where operations execute on rectangular " modules " formed of electrodes. However, non-regular application-specific architectures are common in practice. Hence, we have proposed an approach to the synthesis of application-specific architectures, such that the cost is minimized and the timing constraints of the application are satisfied. ii We propose an algorithm to build a library of non-regular modules for a given application-specific architecture, so that the area of a non-regular application-specific biochip can be used effectively. During fabrication, DMBs can be affected by permanent faults, which may lead to the failure of the application. Our approach introduces redundant electrodes to synthesize fault-tolerant architectures aiming at increasing the yield of DMBs. We also propose a method to estimate, at design time, the application completion time in case of permanent faults in order to verify if an application can be successfully run on the architecture. The proposed approaches were evaluated using several real-life case studies and synthetic benchmarks. Resumé Mikrofluidiske biochips erstatter i stigende grad konventionelle biokemiske analyser ved at integrere alle nødvendige operationer i den biokemiske analyse på en enkelt biochip. Digitale Mikrofluidiske Biochips (DMBs) manipulerer små diskrete maengder Den udviklingsmetode er blevet evalueret på adskillige virkelige case-studier og syn-tetiske benchmarks. in fulfillment of the requirements for acquiring the Ph.D. degree in computer …

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Compilation and Synthesis for Fault-Tolerant Digital Microfluidic Biochips

Our bodies are home to an unseen ecosystem of microbes that live in symbiosis with us. In this work, we extend the “human” in Human-Computer Interaction (HCI) to include these microbes. Specifically, we explore the skin microbiome as an intimate material for interaction design. Viewing the body as a microbial interface, we start by presenting a method to grow our microbiome such that it becomes visible to the human eye. We then present a design space that explores how different environmental parameters, such as temperature and growth media, can be controlled to influence the color of the microbiome. We further investigate how our interactions in a daily uncontrolled environment (e.g., exercising, hugging, typing) can impact the microbiome. We demonstrate several wearable applications that reveal and control the microbiome. Lastly, we address the challenges and opportunities of working with the microbiome as an intimate, living material for interaction design.

µMe: Exploring the Human Microbiome as an Intimate Material for Living Interfaces

Even though bacteriophages are the most plentiful organisms on Earth, many of their genomes and assemblies from metagenomic sources lack protein sequences with identified functions. Most proteins in bacteriophages are structural, known as Phage Virion Proteins (PVPs), but a considerable number remain unclassified. Complicating matters further, conventional lab-based methods for PVP identification are time-consuming and tedious. To expedite the process of identifying PVPs, machine-learning models are increasingly being employed. While existing tools have developed models for predicting PVPs from protein sequences as input, none of these efforts have built software allowing for genomic and metagenomic as input. In addition, there isn’t a framework available for easily curating data and creating new types of models. In response, we introduce PhageScanner, an open-source platform that streamlines data collection, model training and testing, and includes a prediction pipeline for annotating genomic and metagenomic data. PhageScanner also features a graphical user interface (GUI) for visualizing annotations on genomic and metagenomic data. We also introduce a BLAST-based classifier that outperforms ML-based models (achieving an F1 score of 94% for multiclass PVP detection and 97% for binary PVP detection) and an efficient Long Short-Term Memory (LSTM) classifier. We showcase the capabilities of PhageScanner by predicting PVPs in six previously uncharacterized bacteriophage genomes. In addition, showing the utility of the framework, we create a new model that predicts phage-encoded toxins within bacteriophage genomes.

Mirela Alistar

Papers

Module-Based Compilation with Reconfigurable Operation Execution

When Embedded Systems Meet Life Sciences: Microfluidic Biochips For Real-Time Healthcare

Compilation and Synthesis for Fault-Tolerant Digital Microfluidic Biochips

µMe: Exploring the Human Microbiome as an Intimate Material for Living Interfaces

PhageScanner, a flexible machine learning pipeline for automated bacteriophage genomic and metagenomic feature annotation