This Supporting information includes interactive plots, videos, and data captured while performing evaluation and validation experiments for our paper. 

This Supporting Information includes:[Use this section to include additional explanatory text such as an extended technical description of results, full details of mathematical models, etc.]  Corresponding author Email:  [email protected]

This supporting information includes details of the computational model, supplementary figures, interactive 3D CAD model of DraBot and supplementary movies.  Corresponding author Email:  [email protected]

A document by Yi Jin. Click on the document to view its contents.

Supporting InformationYee-Fun Lim,* Chee Koon Ng, US Vaitesswar, Kedar Hippalgaonkar*

Identifying Clostridioides difficile infection (CDI) patients at risk of mortality or recurrence will facilitate prevention, timely treatment and improve clinical outcomes. We aim to establish an open-access web-based prediction system, which estimates CDI patients’ mortality and recurrence outcomes, and explains the machine learning prediction with patients’ characteristics. Prognostic models were developed using four various types of machine learning algorithms and statistical logistics regression model utilizing over 15,000 CDI patients from 41 hospitals in Hong Kong. The boosting-based machine learning algorithm Gradient Boosting Machine (Mortality AUC: 0.7878; Recurrence AUC: 0.7076) outperformed statistical models (Mortality AUC: 0.7573; Recurrence AUC: 0.6927) and other machine learning algorithms. The open-access prediction system for clinicians to assess and interpret the risk factors of CDI patients is now available at https://www.cdiml.care/. In this article, we explain the development of machine learning models and illustrate how to apply hyperparameter tuning with cross-validation to optimize the model accuracy.

Proprioception, the ability to perceive one’s own configuration and movement in space, enables organisms to safely and accurately interact with their environment and each other. The underlying sensory nerves that make this possible are highly dense and use sophisticated communication pathways to propagate signals from nerves in muscle, skin and joints to the central nervous system wherein the organism can process and react to stimuli. In a step forward to realize robots with such perceptive capability, we propose a flexible sensor framework that incorporates a novel hybrid modeling strategy, taking advantage of computational mechanics and machine learning. We implement the sensor framework on a large, thin and flexible sensor that transforms sparsely distributed strains into continuous surface shape. Finite element (FE) analysis is utilized to determine sensor design parameters, while an FE model is built to enrich the morphological data used in the supervised training to achieve continuous surface reconstruction. A mapping between the local strain data and the enriched surface data is subsequently trained using ensemble learning. This hybrid approach enables real-time, robust and high-order surface shape reconstruction. The sensing performance is evaluated in terms of accuracy, repeatability, and feasibility with numerous scenarios, which has not been demonstrated and reported on such a large-scale (A4-paper-size) sensor before.  

Video S1. Magnetic actuation of the metamaterial on a flat surface. 

Complex environments, such as those found in surgical and search-and-rescue applications, require soft devices to adapt to minimal space conditions without sacrificing the ability to complete dexterous tasks. Stacked Balloon Actuators (SBAs) are capable of large deformations despite folding nearly flat when deflated, making them ideal candidates for such applications. This paper presents the design, fabrication, modeling, and characterization of monolithic, inflatable, soft SBAs. Modeling is presented using analytical principles based on geometry, and then using conventional and real-time finite element methods. Both one and three degree-of-freedom (DoF) SBAs are fully characterized with regards to stroke, force, and workspace.  Finally, three representative demonstrations show the SBA's small-aperture navigation, bracing, and workspace-enhancing capabilities.

This Supporting information includes:1. Component Selection and Performance of SFA2. Actuator Manufacturing and Preparation of Conductive Ink 3. Average Thickness of Conductive Coating layer on PU Foam4. Time Response of the Actuator in Different Modes 5. Characterization and Experimental Setup6. Measurement and Data Analysis7. Design Specifications of Soft Robotic Applications8. Supporting Video Corresponding author Email:  [email protected], [email protected] 

Authors   Xulei Wu1,2, Bingjie Dang2, Hong Wang5, Xiulong Wu1,*, and Yuchao Yang2,3,4,* 1School of Electronics and Information Engineering, Anhui University, Hefei 230601, China. 2Key Laboratory of Microelectronic Devices and Circuits (MOE), School of Integrated Circuits, Peking University, Beijing 100871, China.

This Supporting Information includes: the detailed dimensions of the MRE valves, the dimensions of the four different MRE geometries investigated, and the detailed setup and parameters of the PID controllers used. Corresponding author Email:  [email protected] 

Abstract

This Supporting Information includes: a comparison of the REAL (Robot Ear Accomplished by Laser) with a typical vibration measuring system (Laser Doppler Vibrometers, LDV), frequency response of various materials on REAL and real-time analysis of REAL audio neural network model. Xiaoping Hong  Email:    [email protected]

* Corresponding author. E-mail: X.Z.: [email protected]; L.Z.: [email protected]; B.W.: [email protected]

In this study, we utilize simple light-emitting diodes (LEDs) and photodetectors (PDs) combined with an intelligent shape decoding framework to enable 3D shape sensing of a self-contained flexible substrate. Finite element analysis (FEA) is leveraged to optimize the LED-PD layout and enrich ground-truth data from sparse to dense points for model training. The mapping from light intensities to overall sensor shape was achieved with an autoregression-based model that considers temporal continuity and spatial locality. The sensing framework was evaluated on an A5-sized flexible sensor prototype and a fish-shaped prototype, where sensing accuracy (RMSE = 0.27 mm) and repeatability (Δ light intensity < 0.31% over 1000 cycles) were tested underwater. We validate an affordable alternative to FBG sensors with high-order sensing outputs, where demonstrations are supplemented in the below videos.

This Supporting information includes supporting fugures and videos for our paper.  The information's Fig. 16 is the high-quality version of the article's Supplementary Movie 1.Corresponding author(s) Email:   [email protected]  

Table S1.  Performance of soft pneumatic actuators